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Maybe if you don't want people to call you out for your actions, you could just not do the things you're going to get called out for.

Instead of gaslighting people who point it out.

[PL] Dlaczego wrzucenie pliku jpg do pdf-a nie jest pożądane?

W tym artykule przyglądamy się sytuacji w której klient przysyła strony gazet do druku w JPG, a na prośbę o PDF, nie generuje plików poprawnych dla druku gazet, tylko przysyła JPG zagnieżdżony w PDF.

Właściwie z grubsza możemy chyba rozdzielić dwa typy drukarni: takie w których ważna jest jakość obsługi klienta i takich w których jest to drugorzędne. I nie, nie zamierzamy tutaj nikogo ganić, ani krytykować, ani tym bardziej obrażać i uważać, że jedna jest lepsza od drugiej.

Lepsza wyższa jakość obsługi, czy niższa cena? W rzeczywistości oba rozwiązania są ważne i potrzebne, o ile są dobrze akcentowane, tj. o ile sygnalizowane jest w nich podejście do klienta, co najczęściej ma miejsce. Drukarnie bardzo tanie, często równa się zautomatyzowanie wielu procesów i ograniczenie kontaktu z klientem. Drukarnie droższe podejście często mają inne. Właściwie to najczęściej drukarnie w których bardzo ważna jest obsługa klienta, są skierowane do klientów biznesowych, czyli np. drukarnie wysokonakładowe, bądź proponujące nieszablonowe rozwiązania, więc są z myślą o klientach, którzy wykonują zlecenia droższe i nauczeni doświadczeniem wolą zapłacić więcej, aby mieć większą pewność, że pliki zostaną wydrukowane poprawnie. Zaś te, w których obsługa jest drugorzędna, najczęściej skierowane są do klienta masowego, detalicznego, który oczekuje niskich cen, a w przypadku błędów w druku, strata jest na tyle mała (np. druki detaliczne za kilkadziesiąt zł), że klient wykona odpowiednie poprawki i wydrukuje publikację raz jeszcze.

Druk najtańszy ostatecznie nie zawsze najtańszy Zdarzają się oczywiście klienci, którzy poszukują najtańszych rozwiązań, nie zważając na jakość obsługi i kończy się to często tak jak w przypadku jednej z firm, która sprzedawała produkt w jednej z sieci marketów w pudłach na których wydrukowane były pliki podglądowe, czyli wszystko było rozpikselowane i ze znakami wodnymi. Druk był wysokonakładowy, więc najpewniej wykonywany w Chinach (bo pewnie taniej), gdzie nikt nie zwracał uwagi na jakość. I choć ostatecznie to klient odpowiada za przesłane materiały, to w drukarniach nie najtańszych takie rzeczy są wychwytywane i zwracane do zmiany – po to, aby produkt, jaki otrzyma klient był wysokiej jakości, bo klient zadowolony, to klient powracający.

Specyfika druku gazetowego/prasowego Ten zawiły wstęp na temat działania drukarni, w tym drukarni gazetowych i prasowych, nie pojawia się bez powodu – druk gazetowy, prasowy to druk przede wszystkim biznesowy i wysokonakładowy, droższy niż kilkadziesiąt, czy kilkaset zł. Ważne zatem dla klienta powinno być to, aby jakość obsługi była na wysokim poziomie. Z takiego założenia wychodzimy przynajmniej w drukarni gazetowej DWP, gdzie weryfikacja materiałów to jeden z ważniejszych kroków. I nie bez powodu. Ilość wychwytywanych błędów, może być liczona w setkach jak nie tysiącach. Od rozpikselowanych zdjęć, nawet po błędy ortograficzne, bądź techiczne (jak niezmieniona data w gazecie). Staramy się takie rzeczy wychwytywać… i dlatego zwracamy szczególną uwagę na to, czy czcionki w pliku są zagnieżdżone poprawnie, tj. powinny być zamienione na „krzywe”. Więc na pewno nasze usługi nie są najtańsze na świecie, ale porównując straty jakie zaoszczędziliśmy Klientom z powodu wychwyconych błędów – na pewno warte są ceny.

No, ale właściwie po co taki przydługi wstęp i informacja i procesie weryfikacji? Ponieważ jeśli profesjonalne studia graficzne i graficy podsyłają pliki zgodne ze specyfikacją, to osoby nie zajmujące się taką grafiką mają z tym problem. Działy graficzne naszej drukarni gazetowej DWP starają się wychwytywać największe problemy i instruować jak nanieść poprawę (lub pomagają w tej kwestii), ale jest jeden problem, który pojawia się powszechnie: strona przysłana jako JPG, a po prośbie, aby był to plik PDF, wrzucenie strony JPG do PDF-a. A to nie rozwiązuje problemu.

Specyfika plików PDF w druku gazetowym/prasowym Pliki PDF to standard wymyślony przez firmę Adobe, pierwotnie powstał w konkretnym celu: aby plik finalny wyglądał tak samo na komputerze i w druku, aby grafik, który przesyła plik do druku, otrzymywał później publikację wydrukowaną w identyczny sposób, łącznie z idealnym odwzorowaniem kolorów (np. możliwość dodawania kolorów Pantone). Pliki PDF pozwalają zagnieżdżać czcionki, zamieszczać pliki wektorowe i rastowe. Zatem dobrze przygotowany plik PDF jest gwarancją, że publikacja (a zwłaszcza gazeta) będzie dobrze wydrukowana.

Ponieważ to firma Adobe stoi za formatem PDF, nie powinno dziwić, że narzędzia tej firmy najlepiej radziły sobie z przygotowywaniem tych plików. Bardzo dobrze radzą sobie z nimi programy takie jak Adobe Illustrator, Adobe Indesign, Adobe Reader, Adobe Acrobat. Oczywiście to nie jedyne programy, jednak przez lata to one wyznaczały standard z racji poprawnego przygotowywania plików. Zwłaszcza za czasów, gdy za pakiety Adobe płaciło się raz, a nie w formie subskrypcyjnej.

Ale nie wszyscy klienci korzystają z programów typu Illustrator, czy Indesign, czy nawet Corel, czy Affinity, część sięga po inne programy, które w różny sposób radzą sobie z przygotowywaniem plików do druku, czasem wręcz nie radzą. Pozornie plik wygląda w porządku, ale przyglądając się technicznie bliżej, może okazać się, że plik zawiera mankamenty, które mogą spowodować, że wydruk gazety będzie z błędami.

Rasteryzacja strony gazety to błąd Najczęstszym problemem jest sytuacja w której program graficzny spłaszcza i rasteryzuje taką stronę gazety w PDF. Co oznacza „rasteryzacja”? Zamiana wszystkiego na piksele. Pozornie wszystko jest w porządku, w praktyce otrzymujemy plik JPG zamieniony na PDF. Taki plik traci jedną z największych zalet pliku PDF jakimi jest bez wątpienia zagnieżdżenie czcionek, bądź (co bardziej bezpieczne) przechowywanie czcionek w formie „krzywych”. Dlaczego? Grafika rastowa to około 300 DPI, grafika wektorowa to w końcu nieskończenie wiele DPI. Dlatego tak ważnym jest, aby tekst nie był zrastowany.

Całość artykułu dostępna jest na stronie: https://drukarnia.waw.pl/a/ciekawe-dlaczego-wrzucenie-pliku-jpg-do-pdf-a-nie-jest-pozadane/

Genuinely feel that patriarchal structures have enforced a “solve problem” mindset for every conversation. Which is so fucking obnoxious. I want to be able to name a collective problem and discuss it without asking for someone to solve my personal issue.

When I'm trying to explain why there are a lot of resistant people to, say, switching to Linux and how that could be addressed via a collective strategy? And I'm like, “Look, if Linux users just did like monthly or bi-monthly 'Linux Days' somewhere and had a bunch of laptops with a bunch of Linux distros installed? And people could come use them and ask questions and actually discuss it and get comfortable with it? I bet you'd see a lot more people getting involved. Especially if it was geared and marketed toward low-information people instead of Linux Veterans.”

I don't fucking need you to be like, “Well, I could set up some distros on a USB so that you can try them and see which one you like before you install it.”

First, I didn't ask for that. Second, it doesn't fucking address the goddamned problem that we were having a conversation about! You want to know why people don't switch, but you also want to personally thrust your solution (THAT I DIDN'T ASK FOR) in my face.

Cis men, collect yourselves and realise that your style of communication? Is not the rest of ours. And if you're not a cis man and do this, also collect yourselves and unlearn some patriarchal communication structures.

Here's a simple way to start: Before you start giving unsolicited advice, ask a question about the purpose of the conversation and the goals of the other person. “Do you need help installing Linux, and do you want my opinions or assistance?” will go over much better than just trying to solve a problem that no one asked for help with (especially because they've likely had THAT VERY SOLUTION thrown at them any time they try to be like, “Yo, I think the so-called activism of Linux dweebs to getting people to switch away from Windows is pretty garbage for these reasons...”).

Sztuczka przydatna małym wydawnictwom / firmom, które nie mają pod ręką drukarki A3, a chciałyby wydrukować w skali 1:1 szczotki, tj. próbny druk gazetowy:

Druk A3 na drukarce A4? To możliwe: Czy możliwe jest wydrukowanie strony w formacie A3, gdy do dyspozycji mamy tylko drukarkę A4? Dzięki tej poradzie… jak najbardziej i to w programach udostępnianych za darmo.

Ta sztuczka przyda się nie tylko Klientom, którzy drukują gazetę w klasycznym formacie (czyli ok A3), ale każdemu, kto chciałby zobaczyć podgląd strony przeznaczonej do druku „na żywo” w skali 1:1. Dzięki tej podpowiedzi, nie trzeba już kupować drukarki A3, przynajmniej dopóki w ten sposób będą drukowane publikacje sporadycznie (chodzi tylko o wygodę). Ale po kolei!

Standardowy druk gazetowy, jak oferujemy w ramach usług jest w formacie zbliżonym do A3. Niestety, pracownicy większości firm i Urzędów z jakimi mamy przyjemność współpracować… nie posiadają pod ręką drukarki o takim formacie. Zatem, czy pozostaje tylko wydrukowanie podglądu strony pomniejszonej o połowę? Na szczęście dzięki bezpłatnemu programowi Adobe Reader, możemy wydrukować publikację w formacie A3 pomimo posiadania tylko drukarki A4.

Jak to możliwe? Program Adobe Reader po prostu podzieli stronę na pół. Pół strony (czyli część górną) wydrukuje na jednej kartce, a drugie pół na dolnej.

Całość: Jak wydrukować stronę A3 (np. wstępny druk gazety) na drukarce A4, krok po kroku: https://gazety.org/blog/druk-a3-na-drukarce-a4-to-mozliwe/

Why a Career in Data Analytics is a Smart Choice for the Future

In today's digital age, data is a valuable asset driving decisions, innovations, and growth in nearly every industry. From healthcare to finance, marketing to logistics, the ability to extract meaningful insights from vast amounts of data has become indispensable. As a result, data analytics has emerged as one of the most sought-after careers in the modern workforce. If you’re contemplating a career change or aiming to enhance your professional skills, a career in data analytics offers numerous opportunities for growth, development, and stability.

Understanding the Growing Demand for Data Analysts The demand for data analysts has skyrocketed as businesses and organizations increasingly rely on data to inform their strategies. With the explosion of data from various sources—such as social media, customer transactions, and IoT devices—companies are eager to hire professionals who can make sense of this information. Data analysts help businesses understand trends, identify patterns, and make data-driven decisions that improve operational efficiency, customer experience, and profitability.

Industries across the board, including finance, healthcare, retail, and technology, are leveraging data analytics to optimize their processes and gain a competitive edge. As the need for data-driven insights grows, so does the demand for skilled data professionals. This creates an exciting opportunity for those interested in pursuing a career in this field.

Key Trends Shaping the Future of Data Analytics As technology evolves, so does the field of data analytics. Here are some emerging trends that are shaping the future of the industry:

Artificial Intelligence and Machine Learning Integration: AI and machine learning are increasingly being integrated with data analytics tools to help professionals analyze large datasets and predict future trends more accurately. Machine learning models can identify patterns that traditional analytics might miss, making it easier for companies to anticipate market changes and customer needs.

Data Visualization Tools: The rise of advanced data visualization tools has made it easier for businesses to present complex data in a more understandable format. Interactive dashboards and visual reports allow stakeholders to grasp insights quickly and make informed decisions based on real-time data.

Cloud Computing: With the growing volumes of data being generated, cloud computing has become essential in storing, processing, and analyzing large datasets. Cloud-based platforms enable businesses to scale their data analytics operations without having to invest in costly infrastructure.

Increased Focus on Data Privacy: As data collection practices become more sophisticated, there is an increased focus on data privacy and security. Governments worldwide are enacting stricter data protection laws, making it crucial for data analysts to understand compliance and privacy regulations.

Real-Time Analytics: Real-time data analytics is becoming more prevalent in industries such as finance, e-commerce, and logistics. Companies now expect immediate insights to respond to customer behavior, market conditions, or operational issues as they happen.

Essential Tools Every Data Analyst Should Know To succeed as a data analyst, it's essential to be familiar with a variety of tools and technologies. Here are some of the most widely used tools in the industry:

Microsoft Excel: While Excel may seem basic, it remains one of the most powerful tools for data manipulation and analysis. Its functions, such as pivot tables and complex formulas, are invaluable for analyzing datasets.

SQL (Structured Query Language): SQL is the standard language for querying databases. A data analyst uses SQL to extract and manipulate data from relational databases, making it a critical skill for anyone in the field.

Python and R: These programming languages are highly regarded in data analytics. Python is known for its simplicity and versatility, while R is particularly useful for statistical analysis. Both languages offer numerous libraries and frameworks that can be leveraged to carry out complex data analysis tasks.

Power BI and Tableau: These data visualization tools allow analysts to create interactive reports and dashboards that help stakeholders easily interpret data insights. Both Power BI and Tableau offer user-friendly interfaces, making them ideal for non-technical audiences to grasp complex data patterns.

Hadoop and Spark: For analysts working with big data, tools like Hadoop and Apache Spark enable distributed processing of large datasets. These tools are especially useful in industries that require high-volume, real-time data analysis.

Google Analytics: For analysts in marketing or e-commerce, Google Analytics provides valuable insights into web traffic, user behavior, and conversion metrics, enabling companies to optimize their online presence and marketing strategies.

How Data Analyst Training Courses Can Boost Your Career With the increasing demand for skilled data professionals, many individuals are looking for ways to break into the field or improve their existing skills. One of the most effective ways to do this is by enrolling in a data analyst syllabus. These courses are designed to provide the foundational knowledge and hands-on experience required to succeed in the field.

A data analyst training course typically covers key topics such as data cleaning, data visualization, statistical analysis, and the use of popular tools like Excel, SQL, and Tableau. Additionally, many courses are now offered online, giving you the flexibility to learn at your own pace while balancing your professional commitments.

For those looking to specialize further, there are also more advanced programs focusing on machine learning, big data analytics, and artificial intelligence. These can help you stay ahead of industry trends and develop expertise in high-demand areas.

Moreover, completing a data analytics training course can open doors to a wide variety of career opportunities. Whether you want to work in business intelligence, financial analysis, marketing analytics, or healthcare analytics, having the right training will give you the skills needed to excel in these roles. Employers are more likely to hire candidates who have the formal education and technical expertise to hit the ground running, making training an essential investment in your career.

Data analytics is an exciting and fast-growing field with a wide range of applications across industries. As more businesses recognize the value of data-driven decision-making, the demand for skilled data analysts continues to rise. By keeping up with industry trends, mastering the latest tools, and pursuing a data analytics syllabus, you can position yourself for a rewarding career in this dynamic sector. Whether you're looking to break into the field or advance your current role, there’s no better time to explore the world of data analytics and discover the opportunities it holds.

Is Data Analytics a Promising Career Path?

In today’s data-driven world, data analytics has emerged as one of the most sought-after fields, offering vast opportunities for professionals. From small businesses to large corporations, organizations are increasingly relying on data-driven insights to make informed decisions and gain a competitive edge. As a result, the demand for skilled data analysts has surged, making it a lucrative and stable career choice. But is data analytics a good career? In this article, we explore the importance of data analytics, the emerging trends, essential tools in the industry, and how you can benefit from specialized training to enter or advance in this field.

The Importance of Data Analytics in Various Industries Data analytics is revolutionizing industries by enabling businesses to unlock valuable insights from raw data. Whether it's healthcare, finance, retail, or technology, data analytics plays a crucial role in improving operational efficiency, customer experience, and decision-making. For instance, in the healthcare sector, data analytics helps predict patient outcomes, optimize treatment plans, and manage resources effectively. In finance, it is used to detect fraud, manage risks, and forecast market trends. Retailers rely on data analytics to understand consumer behavior, optimize inventory, and personalize marketing efforts.

The versatility of data analytics means it can be applied across a wide range of sectors, which is one of the reasons the field has become so attractive to job seekers. As companies increasingly rely on data to drive their strategies, the need for skilled data professionals is at an all-time high.

Emerging Trends in Data Analytics The field of data analytics is continuously evolving, with new trends and technologies shaping the landscape. Staying updated with these trends is essential for anyone looking to build or advance their career in data analytics.

Artificial Intelligence and Machine Learning Integration The integration of AI and machine learning with data analytics is transforming the way businesses process and interpret data. AI algorithms are now being used to automate data analysis, uncover hidden patterns, and even make predictions. This advancement allows data analysts to focus on interpreting the insights, leaving repetitive tasks to machines.

Big Data With the exponential growth of data generated by businesses, big data analytics has become a focal point. Professionals who are skilled in handling massive datasets and can work with tools such as Hadoop and Apache Spark are in high demand. Big data analytics offers companies the ability to analyze vast quantities of data from various sources, providing deeper insights and more accurate forecasts.

Data Visualization As businesses generate more complex data, the need for data visualization tools is increasing. Data visualization helps analysts communicate insights in a clear, easily understandable manner. Tools like Tableau and Power BI are frequently used to create interactive dashboards and reports that allow decision-makers to quickly grasp key findings.

Cloud Computing Cloud platforms are increasingly being used to store, process, and analyze data. This trend is driving demand for professionals who are proficient in cloud-based tools and services like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Cloud computing enables organizations to scale their data operations efficiently and access real-time data for more timely decision-making.

Data Privacy and Ethics With the growing importance of data, ensuring data privacy and ethical use has become a major concern. Data analysts are now expected to adhere to data protection laws such as the General Data Protection Regulation (GDPR) and ensure that data is used responsibly. Ethical considerations are crucial, especially when handling sensitive information.

Essential Tools in Data Analytics A data analyst’s role requires proficiency in various tools and software to collect, process, and analyze data. Here are some of the most important tools used in the industry:

Excel Microsoft Excel remains one of the most widely used tools for data analysis, especially for smaller datasets. Its flexibility and ease of use make it a fundamental tool for anyone starting in the field. With advanced features like pivot tables, data visualization options, and formula functions, Excel can handle a wide range of analytical tasks.

SQL Structured Query Language (SQL) is essential for working with databases. It allows data analysts to extract, manipulate, and analyze data stored in relational databases. SQL skills are crucial for any data analyst, as it enables them to work with large volumes of structured data.

Python and R Python and R are two of the most popular programming languages used in data analytics. Both are powerful tools for data manipulation, statistical analysis, and machine learning. Python is known for its versatility and ease of use, while R is widely favored in academia for its statistical analysis capabilities.

Tableau and Power BI As mentioned earlier, data visualization is an essential skill for modern data analysts. Tools like Tableau and Power BI allow analysts to create interactive reports and dashboards that help stakeholders visualize data trends and make informed decisions.

Hadoop and Apache Spark For those working with big data, platforms like Hadoop and Apache Spark are indispensable. These tools help analysts process and analyze large datasets that cannot be handled by traditional data-processing methods. They are particularly useful for managing unstructured data and running complex algorithms.

How Data Analyst Training Courses Can Help As the demand for data analytics professionals grows, more individuals are seeking to enter the field. One of the best ways to gain the necessary skills is by enrolling in a data analyst course fees. These programs offer structured learning and hands-on experience with the essential tools and techniques used by professionals in the field.

Whether you are starting from scratch or looking to enhance your existing skills, a training course can help you understand core concepts such as data cleaning, statistical analysis, machine learning, and data visualization. Additionally, many courses provide access to industry-recognized certifications, which can improve your employability and career prospects.

Moreover, these courses often offer real-world projects and case studies that allow you to apply your learning in practical scenarios. This hands-on approach not only helps you grasp theoretical concepts but also prepares you for the challenges you will face in the workplace.

Data analytics is undoubtedly a promising and rewarding career path, offering vast opportunities for growth and advancement. The importance of data in business decision-making continues to increase, making data analysts essential across various industries. With the right training and tools, professionals can position themselves for success in this dynamic field.

For those interested in entering the world of data analytics, enrolling in a data analytics course fees can provide the foundational knowledge and practical skills needed to thrive. Whether you are new to the field or looking to upgrade your expertise, these courses can help you stay ahead in a rapidly changing industry.

《華爾街日報》發表專題報導，題為＜北京如何控制香港的金融中心——並把西方拋諸腦後＞。

報導主要談及香港金融業至少今年以來已出現的趨勢，一個很多香港人都知道的事情——香港的IPO、其他集資活動等金融活動，已由大陸銀行和銀行家主導，西方銀行佔有率大降，西方金融機構近年減少香港人手，這並非中國近期經濟轉弱的周期性問題，而是香港金融業結構出現改變。 報導稱，香港正由「泛亞洲」投資銀行中心變成中國投資銀行中心，現在的香港「真的只是中國市場而已」。

《華》用了很多例子和數據來反映這個現象，文首一開始說，港交所行政總裁陳翊庭今年春季在港交所一個活動用普通話發言，有別於港交所高層通常用英語或廣東話發言的習慣，港交所也沒有提供翻譯。

報導引述一名來自西方國家的銀行家，他說，現在周遭聽到很多普通話，面對中國銀行業的激烈競爭，他在香港感到疏離。

對於中資金融機構主導香港，西方銀行家有微言，一些中資高層對《華》說，這些對中資銀行的批評是廢話，很多客戶選擇中資銀行，因為中資銀行更好地服務客戶、收費較低、在大陸有更佳網絡。

報導提及中資和西方金融機構做法的一個分別：中資銀行協助進行集資活動時，接受客戶公司自己找一大班「親朋戚友」「捧場」，國際金融機構拒絕這樣做，因為會削弱盡職審查，堅持要找一定數量的獨立投資者。中國銀行家戲稱外資銀行這個心態是「道德強迫症」（moral obsessive-compulsive disorder）。

【老中人又在赢了。你们在大陆有更好的网络，不就缘于你爹CCP严厉限制外资银行在华开展业务？不了解可自己问AI。moral obsessive-compulsive disorder就更搞笑了，不是自认没道德没底线吗？这果然是老中的竞争优势。】

Tongkruid wil zijn zoals het klinkt: van tong tot vruchtbare grond, om niet meer te vergaan. Onkruid, van onderuit, verandert de wereld in een nieuwe vruchtbaarheid, lang nadat alle bovenwereldse poespas vervaagd is in de annalen van een vergankelijk systeem, dat aan haar eigen verdelgers ten onder dreigt te gaan. Ideeën komen, ideeën gaan, tongkruid zal nooit vergaan.

Tussen het plankier van de morrelende loszittende stenen die het patriarchaat en het kapitaal toevallig vergat te onderhouden, ontluikt stiekem een groen sprietje, een eerste zin, een tweede. Weldra zitten we drie zinnen ver in de tweede paragraaf. Hopla, weer een regel. Ergens diep of ondiep onder die halfstenen, half vergane ondergrond zit een kiem vol ideeën te broeden en rijpen, tot haar klanken je oog zullen strelen. Niemand weet nog welke vorm dit prille tongkruid zal krijgen, maar een en ander is al vrij duidelijk: het zal weerbarstig zijn, soms onopgemerkt de omgeving opmerken. Gadeslaan wat er te slaan valt, of zich terugtrekken in een zorgzame spleet waar de voeten van het patrikapitaliarchaat het niet kunnen kwetsen. Zo houdt tongkruid zichzelf in stand temidden van vlagen van de dwang-woede zo typisch voor het normatieve systeem daarbuiten.

Welkom in deze prille beginfase. Tongkruid is ontsproten, en zoals het onkruid betaamt, zal haar tong nooit vergaan. Hou ons maar in 't oog, zodat we je kiezelterras overwoekeren met leven, je prikkelen met onze distels van gedachten, wie weet zelfs een waardig plekje als bloem in de tuin van je hart veroveren. Wat nu niet meer is dan een kriebel onder je blote voeten op het pad, kan zo groot worden dat je in haar schaduw zitten kan. Heilzaam en tegendraads. Tongkruid kondigt zich aan!

Is Pursuing a Data Analyst Career Even Worth It?

In today’s rapidly evolving job market, career decisions can be daunting. One field that has consistently attracted attention is data analytics. As organizations across industries seek to leverage data for better decision-making, the demand for skilled data analysts has skyrocketed. But with the rise of automation, artificial intelligence, and the complexities of the digital landscape, is pursuing a career in data analytics truly worth it?

In this article, we’ll explore whether a data analyst career is a sound choice, covering key benefits, industry trends, essential tools, and how a data analytics training institute can enhance your career prospects.

The Growing Importance of Data Analytics

The importance of data analytics has never been greater. In fact, data is often referred to as the “new oil,” and for good reason. From healthcare to marketing, finance to sports, data is central to decision-making processes. Data analysts are the professionals who transform raw data into actionable insights, helping companies optimize their strategies and achieve business objectives.

Why is Data Analytics So Valuable?

Informed Decision-Making One of the main reasons companies invest in data analytics is that it leads to better decision-making. Data analytics helps organizations uncover trends, patterns, and correlations that are otherwise hidden, allowing them to make data-driven decisions rather than relying on intuition. This not only improves efficiency but also reduces risks associated with poor decision-making.

Cost Efficiency and Profit Optimization By analyzing data, businesses can identify inefficiencies and areas for cost reduction. For example, a data analyst working in supply chain management can spot inefficiencies that result in wasteful spending or delays. Similarly, financial analysts use data to optimize investment strategies and maximize profits.

Competitive Advantage Companies that effectively utilize data have a competitive edge. From personalized marketing strategies to predictive analytics that anticipate market shifts, organizations that embrace data analytics can stay ahead of competitors. Data analysts are key players in maintaining and enhancing this advantage.

Benefits of Pursuing a Data Analyst Career

1. High Demand and Job Security The demand for data analysts has been growing steadily for the past decade, and it shows no signs of slowing down. According to the U.S. Bureau of Labor Statistics, the need for data analysts and similar roles (such as data scientists and business intelligence analysts) is expected to grow by 25% through 2030. This is significantly faster than the average growth rate for other professions, which speaks to the increasing importance of data across industries.

2. Attractive Salaries A career in data analytics is also financially rewarding. According to Glassdoor, the average salary for a data analyst in the U.S. is around $70,000 per year, though this can rise significantly with experience or specialization. Senior analysts, especially those working with big data, machine learning, or predictive analytics, can earn upwards of $100,000 annually. Additionally, the flexibility to work in a wide variety of industries further enhances salary potential.

3. Diverse Career Paths One of the most attractive aspects of a career in data analytics is its versatility. Data analysts can work in almost any industry, including:

Finance and Banking: Optimizing investment portfolios, managing risk, and analyzing financial trends. Healthcare: Improving patient outcomes through data-driven research and cost optimization. Retail and E-Commerce: Personalizing customer experiences and optimizing inventory. Sports and Entertainment: Analyzing performance data and fan engagement. Government and Public Policy: Using data to drive public decision-making and improve efficiency. With the right skills, data analysts can move into more specialized roles such as data scientist, business intelligence analyst, or machine learning engineer. The field is dynamic and offers plenty of opportunities for growth and advancement.

1. Impactful Work Working as a data analyst also means contributing to meaningful projects. Whether it’s helping a nonprofit reduce its operational costs, improving patient care in a hospital, or assisting a business in expanding its market reach, data analysts often play a key role in the success of their organizations.

Essential Skills and Tools for Data Analysts

To succeed in a data analyst career, you need to develop a strong set of technical and soft skills. Here are the essential tools and competencies required:

1. Technical Skills Excel: While it may seem basic, Excel remains one of the most important tools for data analysis. From simple calculations to complex pivot tables, Excel’s versatility makes it indispensable for many data analysts. SQL: SQL (Structured Query Language) is essential for querying and extracting data from relational databases. Knowing how to write complex SQL queries allows data analysts to access and manipulate data effectively. Python and R: These programming languages are commonly used for data analysis, especially for handling large datasets, performing statistical analysis, and building predictive models. Data Visualization Tools: Tools like Tableau and Power BI allow analysts to create compelling visual reports that present data in a way that’s easy to understand for business stakeholders. Google Analytics: For analysts working in marketing or e-commerce, proficiency with tools like Google Analytics is essential for measuring website performance and customer engagement.

2. Soft Skills Critical Thinking and Problem-Solving: The ability to analyze data and extract meaningful insights is crucial for success in this field. You need to be able to think critically and come up with data-driven solutions to business problems. Communication: Data analysts often work with non-technical stakeholders, so it’s important to communicate complex data insights clearly and concisely. Strong presentation and reporting skills are key. Attention to Detail: Data analysis requires precision. A small error in data collection or interpretation can lead to incorrect conclusions and costly mistakes.

Emerging Trends in Data Analytics

As the field of data analytics continues to evolve, there are a few emerging trends that are shaping the future of the profession:

1. Big Data and Cloud Computing As data becomes more voluminous and complex, handling large datasets through traditional tools like Excel is becoming increasingly impractical. Cloud platforms like AWS and Google Cloud are helping businesses store and process vast amounts of data more efficiently. As a result, data analysts need to be familiar with cloud computing and big data tools like Hadoop and Spark.

2. AI and Machine Learning Data analysts are increasingly working alongside machine learning models to generate predictions and automate data processes. Machine learning algorithms are being integrated into business strategies to predict customer behavior, optimize marketing campaigns, and even prevent fraud. Understanding machine learning basics is becoming a must for data analysts who want to stay ahead of the curve.

3. Real-Time Data Analytics Real-time data analytics allows organizations to make immediate decisions based on live data. As the speed at which data is generated increases, analysts need to be able to process and analyze data in real time, helping businesses respond more quickly to changes in the market or customer behavior.

How a Data Analyst Training Course Can Help

If you’re considering a career in data analytics, taking a data analyst training institute is an excellent way to get started. These courses provide you with hands-on experience in the essential tools and techniques used in the field. Whether you’re learning to use SQL for database management, building data models in Python, or mastering data visualization, a structured training program can help you acquire the skills necessary to succeed.

Additionally, completing a certification program can boost your credibility and make you more competitive in the job market, opening doors to higher-paying opportunities.

In short, a career in data analytics is not only worth it but is increasingly vital in today’s data-driven world. The demand for skilled analysts is high, the earning potential is attractive, and the work is both meaningful and impactful. With emerging technologies, evolving trends, and the need for data-driven decision-making, the field is poised for continued growth.

If you’re detail-oriented, enjoy problem-solving, and have an interest in data, then pursuing a career as a data analyst may be the perfect fit for you. Whether you're just starting or looking to enhance your skill set, enrolling in a data analytics training will give you the foundation you need to thrive in this exciting and rewarding field.

Should I Pursue a Career in Data Analytics?

The data revolution is here, and it’s not slowing down anytime soon. From healthcare to finance, marketing to sports, industries across the globe are increasingly reliant on data to make informed decisions. If you’ve ever wondered whether a career in data analytics is the right path for you, you're not alone. Data analytics is one of the fastest-growing fields, and many professionals are shifting gears to explore opportunities in this exciting domain.

In this article, we’ll explore what it takes to pursue a career in data analytics, the tools used by professionals, emerging trends in the industry, and how enrolling in a best data analyst certification can set you up for success.

What is Data Analytics?

At its core, data analytics involves the process of examining raw data to draw conclusions and make decisions. A data analyst uses various techniques to clean, organize, and analyze data, ultimately turning it into valuable insights. These insights help organizations optimize processes, identify opportunities for growth, mitigate risks, and enhance decision-making.

Data analytics is integral in nearly every industry today. For example:

In marketing, data analytics can help businesses understand customer behavior and tailor their campaigns for maximum engagement. In healthcare, it can help doctors and administrators make better decisions about patient care and resource allocation. In finance, it helps firms forecast market trends, optimize investment strategies, and assess risks. In sports, data analysts use performance metrics to help teams and coaches make data-driven decisions on player selection and game strategy. Given its importance across multiple sectors, it’s clear that data analytics offers a range of exciting career possibilities.

Key Skills for a Career in Data Analytics

If you’re considering a career in data analytics, there are certain skills you’ll need to master to succeed. Here’s a look at the key areas of expertise that will set you apart as a data professional:

1. Technical Skills The foundation of data analytics is built on technical abilities. You’ll need proficiency in programming languages, databases, and analytical tools. The most common technical skills include:

SQL (Structured Query Language): SQL is a must-have for querying relational databases. It allows data analysts to extract, manipulate, and aggregate data effectively. Python & R: These programming languages are essential for advanced data analysis. They offer libraries and frameworks for data manipulation (like Pandas), data visualization (Matplotlib and Seaborn), and machine learning (SciKit Learn and TensorFlow). Excel: While Excel might seem basic, it’s still one of the most widely used tools for data analysis, especially for tasks like creating reports, building dashboards, and performing basic statistical analysis.

1. Analytical Thinking Being able to think analytically is crucial in data analytics. Analysts need to make sense of vast amounts of data and find patterns, trends, and anomalies that are often hidden beneath the surface. A strong problem-solving mindset will allow you to approach data from different angles and come up with innovative solutions.

2. Data Visualization Skills Turning raw data into a story that others can understand is one of the most valuable skills in data analytics. Proficiency in data visualization tools such as Tableau, Power BI, and Google Data Studio allows analysts to present complex data insights in an easy-to-understand format. Effective visualizations, like charts, graphs, and dashboards, help stakeholders make decisions quickly.

3. Business Acumen It’s essential for a data analyst to understand the business context in which they’re working. A deep understanding of the industry, organizational goals, and how data fits into broader business strategies is key to making valuable contributions. This helps analysts interpret data correctly and recommend data-driven strategies that align with business objectives.

Emerging Trends in Data Analytics

The field of data analytics is evolving rapidly, and staying on top of emerging trends is crucial for anyone pursuing a career in this area. Here are a few trends shaping the industry in 2025 and beyond:

1. Artificial Intelligence (AI) and Machine Learning AI and machine learning are transforming the way data is analyzed. By using algorithms to learn from data and make predictions, AI is automating complex processes and uncovering insights that would have been difficult or impossible for humans to find. Data analysts are increasingly leveraging these technologies to improve their work, whether it’s predicting customer behavior or optimizing supply chains.

2. Big Data The rise of big data has changed the landscape of data analytics. With more data being generated than ever before, traditional methods of data storage and processing are no longer sufficient. Analysts are now working with larger, more complex datasets (sometimes referred to as the “three Vs” of big data: volume, velocity, and variety). Familiarity with big data tools like Hadoop and Apache Spark is becoming increasingly valuable.

3. Real-Time Analytics As businesses aim for faster decision-making, real-time data analytics is becoming a necessity. Companies now need to process and analyze data as it’s generated, allowing them to take immediate action based on insights. Whether it’s monitoring website traffic or tracking inventory in real time, data analysts need to be equipped to handle fast-moving data and deliver insights on the spot.

4. Data Privacy and Ethics With stricter regulations like GDPR and CCPA in place, data analysts need to be aware of data privacy and ethics. Organizations are now required to handle sensitive data in a more transparent and responsible manner. A strong understanding of data protection regulations is essential for anyone working with personal or confidential information.

Tools Used by Data Analysts

The right tools can make all the difference in your data analytics career. Here are some of the essential tools you’ll encounter:

Excel: As mentioned earlier, Excel remains one of the most popular tools in data analysis, especially for handling small to medium-sized datasets. SQL: SQL is crucial for querying and extracting data from relational databases. Python and R: These programming languages allow data analysts to perform advanced analysis, machine learning, and statistical modeling. Tableau and Power BI: These tools are used for creating interactive and dynamic data visualizations and dashboards. Google Analytics: In the digital marketing and e-commerce world, Google Analytics helps analysts track and understand website performance, customer behavior, and traffic sources. Should You Pursue a Career in Data Analytics? If you have an analytical mindset and enjoy working with data, a career in data analytics could be a great fit for you. The field offers numerous opportunities across various industries, competitive salaries, and the chance to work on innovative projects that have a real impact on business success.

However, it's important to understand that data analytics requires a combination of technical and soft skills. If you’re comfortable with numbers, enjoy problem-solving, and have a knack for spotting trends, this career could be highly rewarding. If you’re not yet familiar with the tools or concepts mentioned, taking a best data analytics certification can help you acquire the skills needed to thrive in the field. These courses typically cover the essential tools (like Excel, SQL, and Tableau), as well as data analysis techniques, business intelligence, and statistics.

Data analytics is an exciting, dynamic, and rewarding career path that’s in high demand across all industries. Whether you’re looking to analyze customer behavior, optimize business operations, or predict market trends, a career in data analytics offers endless opportunities. By mastering the necessary tools, keeping up with industry trends, and gaining hands-on experience you can carve out a successful career in this rapidly growing field.

What Does a Financial Data Analyst Do?

In today’s data-driven world, financial institutions and businesses rely heavily on data analysts to make informed decisions, mitigate risks, and drive profitability. Among the various branches of data analysis, the role of a financial data analyst stands out as especially crucial. These professionals work at the intersection of finance and data science, providing actionable insights to help organizations optimize their financial strategies and improve overall performance.

In this article, we will explore the key responsibilities of a financial data analyst, the tools they use, emerging trends in the industry, and how taking a top data analyst certification can help you succeed in this high-demand field.

What Does a Financial Data Analyst Do?

A financial data analyst is a specialized type of data analyst who focuses on interpreting and analyzing data related to finance and investments. Their primary goal is to use data to guide financial decision-making, identify trends, forecast financial outcomes, and support strategic planning.

Key Responsibilities of a Financial Data Analyst

Data Collection and Management Financial data analysts collect vast amounts of financial data from internal sources (such as company financial statements, sales data, and cash flow reports) and external sources (market data, economic indicators, and industry reports). The analyst then organizes and cleans this data to ensure accuracy and reliability. Inaccurate data could lead to poor financial decisions, which is why attention to detail is critical in this role.

Financial Analysis and Reporting Once the data is cleaned, a financial data analyst uses various statistical techniques to analyze trends, assess financial health, and forecast future performance. This might involve calculating key performance indicators (KPIs), such as return on investment (ROI), earnings per share (EPS), and debt-to-equity ratio, among others. Based on the analysis, the analyst prepares reports and presentations to communicate the findings to management and other stakeholders.

Risk Assessment and Management One of the most important tasks for financial data analysts is evaluating potential risks to the business. This could include market volatility, credit risk, liquidity issues, or other economic factors that could impact financial stability. By leveraging historical data and advanced predictive models, financial data analysts can assess and mitigate financial risks, ensuring that companies remain financially resilient in the face of uncertainty.

Financial Forecasting and Budgeting Financial data analysts use historical financial data and industry trends to create forecasts for future performance. This may involve building complex financial models to predict revenues, expenses, and profit margins. Accurate forecasting helps organizations set realistic goals, allocate resources effectively, and plan for future growth. Budgeting also plays a key role in this process, as analysts help companies track their financial performance and adjust their strategies accordingly.

Investment Analysis Financial data analysts often work with investment managers and other financial professionals to evaluate potential investment opportunities. By analyzing market trends, economic conditions, and company performance, they can identify investments that align with an organization’s goals and risk tolerance. This might involve analyzing stock prices, bonds, commodities, or real estate markets.

Data Visualization and Reporting Financial data analysts must be able to present complex financial data in a way that’s easy to understand. To do this, they rely on data visualization tools to create charts, graphs, and dashboards. These visualizations help stakeholders quickly grasp key financial trends, making it easier to make informed decisions.

Tools Used by Financial Data Analysts

A financial data analyst needs a wide range of tools and software to effectively collect, process, analyze, and present financial data. Below are some of the most commonly used tools in the industry:

1. Excel Excel remains one of the most powerful tools for financial analysis. It offers a wide variety of functions, from basic calculations to complex financial modeling. Analysts use Excel to track budgets, analyze profit margins, calculate returns, and more. Advanced features like pivot tables and macros make it an indispensable tool for financial data analysts.

2. SQL SQL (Structured Query Language) is a must-have skill for any data analyst, including those in finance. SQL allows analysts to query large databases and extract relevant financial data. With SQL, financial data analysts can pull transaction records, balance sheets, and other key financial data from relational databases to perform their analysis.

3. Python and R Both Python and R are programming languages that are commonly used for data analysis. Python is particularly popular because of its flexibility and the wealth of libraries available, such as Pandas (for data manipulation), Matplotlib (for data visualization), and Scikit-learn (for machine learning). Python and R can be used to build predictive models, perform advanced statistical analysis, and automate repetitive tasks.

4. Tableau and Power BI Tableau and Power BI are among the most widely used data visualization tools. Financial data analysts use these platforms to create interactive dashboards, charts, and graphs that present financial data in an easy-to-understand way. These tools allow financial analysts to communicate complex financial trends visually, helping decision-makers act quickly and confidently.

5. Financial Modeling Software Tools such as FactSet, Bloomberg Terminal, and Morningstar Direct are essential for financial modeling and analysis. These platforms provide real-time financial data, news, and analytics, which financial data analysts use to evaluate investment opportunities, assess risk, and make forecasts. These tools help analysts stay up-to-date with the latest market trends and economic conditions.

Current Trends in Financial Data Analysis

The field of financial data analysis is rapidly evolving, with several key trends shaping the industry. Staying up-to-date with these trends is essential for financial analysts to remain competitive in the job market.

1. Big Data and Advanced Analytics The increasing availability of large datasets (known as “big data”) is transforming financial analysis. Financial institutions are now using advanced analytics techniques, including machine learning and artificial intelligence, to sift through massive amounts of data and identify trends, risks, and investment opportunities that would have been impossible to uncover using traditional methods.

2. Artificial Intelligence (AI) and Machine Learning AI and machine learning algorithms are becoming essential tools for financial data analysts. These technologies can automatically analyze data, make predictions, and even detect fraud or anomalies in financial transactions. As AI becomes more sophisticated, it will continue to play a larger role in financial decision-making and risk management.

3. Data Automation Automation is increasingly being used in financial data analysis to streamline repetitive tasks such as data collection, cleaning, and reporting. By automating these processes, financial data analysts can focus on more strategic work, such as forecasting and risk assessment. Automation tools can help improve efficiency, reduce human error, and ensure that decisions are based on accurate, real-time data.

4. Blockchain Technology As blockchain technology continues to disrupt the finance industry, financial data analysts are using it to improve data security and transparency. Blockchain provides a decentralized, tamper-proof system for recording financial transactions, which can be particularly useful in areas like accounting, audit, and fraud detection.

How to Become a Financial Data Analyst

If you're interested in pursuing a career as a financial data analyst, taking a data analytics training course or data analyst training course can be a great starting point. These courses teach you essential skills such as data cleaning, financial modeling, and statistical analysis. They also introduce you to the key tools you'll need to succeed, including Excel, SQL, Python, and Tableau.

In addition to formal training, gaining practical experience through internships, projects, or part-time roles is crucial. The more hands-on experience you have, the better equipped you’ll be to handle the complexities of financial data analysis.

A career as a financial data analyst offers exciting opportunities to work at the intersection of finance and data science. Whether you’re analyzing market trends, evaluating investment opportunities, or forecasting financial outcomes, your role will be crucial in helping organizations make data-driven decisions. By mastering the tools and staying updated with emerging trends, you can excel in this dynamic field. Enrolling in a top data analytics certification will equip you with the skills needed to succeed in this high-demand profession and give you a competitive edge in the evolving world of financial data analysis.

why/how

As part of my mental management in recent years I pay careful attention to what goes in my brain. I do not permit careless blatherers to have access to my brain. This prohibition excludes most news organizations, commercial speech, hype for clicks, pointless arguers, Republicans, etc. Many good folks use social media for emotional venting. I certainly do. I'm rearranging what shows in my feeds so that I get what I want to see. For me, I don't have time to read and learn what improves my soul if I'm breathlessly following every bad thing done by the people I already know are very bad.

6 Key Types of Data Analysis for Informed Decision-Making

In today’s data-driven world, decision-makers increasingly rely on data analysis to make informed choices, manage risks, and predict future trends. Data analysis offers a framework for transforming raw information into valuable insights, ultimately guiding business strategies, improving efficiency, and fostering innovation. Whether through a data analyst training course or a data analytics training course, gaining expertise in various types of data analysis can provide essential skills for professionals aiming to support effective decision-making. Here, we will explore six primary types of data analysis, each serving unique purposes and applications in diverse industries.

Descriptive Analysis Descriptive analysis is often the first step in data analysis and focuses on summarizing historical data to provide an understanding of past performance. It answers the question, “What happened?” by presenting trends and patterns based on previously gathered data. This type of analysis is particularly valuable for organizations wanting to gain a clear picture of their current status, such as monthly revenue trends, customer demographic breakdowns, or product sales reports.

For instance, a retail company might use descriptive analysis to assess quarterly sales across regions and products. By summarizing and visualizing this data, decision-makers can identify areas of strong performance and those needing improvement. A data analyst course in pune equips professionals with essential skills in handling and interpreting historical data to conduct this kind of analysis effectively. This foundational knowledge lays the groundwork for more advanced types of analysis.

Diagnostic Analysis Moving beyond understanding “what happened,” diagnostic analysis dives into “why it happened.” This type of analysis examines data in a more granular way to uncover relationships, trends, and factors contributing to specific outcomes. Diagnostic analysis is commonly used in root cause analysis and requires a combination of analytical techniques, often including comparisons and correlations.

Consider a company that notices a sudden dip in customer satisfaction ratings. A diagnostic analysis could help pinpoint potential causes, such as a recent change in customer service protocols or a shift in product quality. By isolating and examining these factors, the organization can better understand what might have triggered the decline in satisfaction. A data analytics training course often covers diagnostic analysis, as it’s essential for professionals seeking to understand complex data relationships that can impact critical business decisions.

Predictive Analysis Predictive analysis is a forward-looking approach that uses historical data, machine learning, and statistical algorithms to forecast future outcomes. This type of analysis is particularly useful for risk management, demand forecasting, and strategic planning. By leveraging patterns and trends in past data, predictive analysis attempts to answer the question, “What is likely to happen?”

For instance, in the financial industry, predictive analysis is used extensively for credit scoring and fraud detection. Banks and lending institutions analyze past customer behaviors to predict potential risks, while marketing departments might forecast customer purchasing patterns to optimize promotional strategies. Skills gained from a data analyst course in hyderabad are crucial in predictive analysis, as professionals learn to work with predictive models and interpret their outcomes accurately.

While predictive analysis can offer valuable insights, it’s essential to remember that predictions are based on probabilities and are never 100% certain. Nonetheless, decision-makers who are skilled in interpreting predictive analysis can make more confident, data-backed decisions.

Prescriptive Analysis Prescriptive analysis takes prediction one step further by providing recommendations on possible actions. It addresses the question, “What should we do about it?” using algorithms, simulations, and optimization techniques. This type of analysis is particularly valuable in complex decision-making situations where multiple variables and potential outcomes are at play.

For example, supply chain managers may use prescriptive analysis to determine optimal inventory levels, considering fluctuating demand, supplier reliability, and shipping costs. The result is a data-driven recommendation that minimizes costs while meeting customer demands. Completing a data analytics training course can significantly enhance one’s ability to conduct prescriptive analysis, as it often involves advanced methods and tools that require specialized training.

The ability to interpret prescriptive analysis can empower decision-makers to take proactive steps rather than simply reacting to data insights. In sectors like healthcare, finance, and logistics, prescriptive analysis is often used to improve operational efficiency and reduce risk.

Exploratory Analysis Exploratory data analysis (EDA) is a process aimed at investigating data sets to identify patterns, anomalies, and relationships without having a specific hypothesis in mind. EDA is particularly useful for finding new insights and framing questions for further analysis. It’s an open-ended approach that often leads to unexpected discoveries, guiding analysts on where to focus their efforts next.

For example, in the healthcare sector, researchers might use exploratory analysis on patient data to find correlations between lifestyle factors and health outcomes. Such insights could be valuable for creating preventive care programs. A data analyst training course introduces techniques like data visualization and statistical tools commonly used in EDA, empowering professionals to look deeper into data and uncover patterns that could be transformative for their organizations.

Inferential Analysis Inferential analysis is used when decision-makers need to draw conclusions about a population based on a sample. Unlike descriptive analysis, which summarizes data, inferential analysis uses statistical techniques to make generalizations and test hypotheses. It’s especially helpful in situations where it’s impractical to collect data from every individual in a population, allowing analysts to work with a manageable subset of data while still achieving meaningful results.

For example, inferential analysis might be employed by political analysts to estimate public opinion based on survey responses from a sample group, rather than polling an entire population. The techniques involved in inferential analysis, such as regression analysis and hypothesis testing, are commonly covered in a data analytics training course, providing learners with the skills necessary to make valid and reliable conclusions.

The Role of Data Analytics Training in Supporting Decision-Making These six types of data analysis each serve distinct functions but collectively contribute to a comprehensive decision-making process. By integrating descriptive, diagnostic, predictive, prescriptive, exploratory, and inferential analysis, decision-makers can gain a well-rounded view of their data, enabling them to act confidently based on evidence and insights.

Through a data analyst course in ahmedabad, professionals can acquire a solid foundation in these types of analysis, learning to apply them across real-world scenarios. Such training fosters the ability to interpret complex data, apply advanced analytical methods, and communicate findings effectively. In today’s fast-paced business environment, the capacity to use data-driven strategies is essential for staying competitive.

Understanding and applying different types of data analysis enables professionals to transform raw data into actionable intelligence. Whether focusing on historical patterns, diagnosing issues, forecasting future events, recommending optimal actions, or uncovering hidden relationships, data analysis provides decision-makers with a powerful toolkit for navigating uncertainty and driving success.

As I see it learned helplessness is a major problem among the not fully terrible people Learned helplessness in response to cognitive dissonance Cognitive dissonance in response to a culture of lies

The abused become perpetrators Without extraordinary effort And to improve is to ostracize oneself

The culture is whiteness The lies are that whiteness exists, that white existence is superior to all other human existence, that America is exceptional (in a good way), that America has ever been more good than bad, that America believes in liberty and justice for all. There are so many lies. The cognitive dissonance is so intense that it causes widespread dysfunction. The cognitive dissonance must either be reckoned with or avoided with great effort. The cognitive dissonance is so strong that avoiding it requires epidemics of drugs, drunkenness, overeating, overspending, waste, violence, sleeplessness, conspiracies and other nonsensical beliefs, and so much more. America is by far the worst nation on a range of issues, and it's much much worse when taking our wealth into account. The current most widespread avoidance technique is never thinking at all. None of these coping strategies result in peace of mind but they do enable the state of cognitive dissonance to continue rather than it being faced and overcome. The cognitive dissonance causes learned helplessness because brains do not work well under the above conditions. Being able to make decisions, knowing what one wants, clear perceptions — all are impaired.

This is the standard status of a majority of my fellow white Americans, in my opinion. I do not speak to the rest of the population because I am not sufficiently familiar with nonwhiteness. Based on voting alone I'm gonna say that other groups are doing their best and the problem is with my fellow white folk. These feelings that I have as an insider raised in affluent white Christian America are why I have no hope for humanity.

Prove me wrong. Please.

Crap, just found that a domain I registered years ago was still autorenewing.

Expensive mistake…

Edit

Of course, I have canceled it already! :)

This text was originally written in French and has been translated, in part, with the help of a translation service. (I'm already practising with one language, so I can't be juggling three at the same time!)

Introduction

Languages are very interesting because they represent the culture and ideology of a people and their way of thinking and transmitting messages. I think it's obvious that I love languages, given my passion for conlangs, which allows me to play with the different ways in which concepts and notions can be articulated in a language, as well as the different ways in which the grammar of a language can be formed.

But what I really enjoy is ‘learning’ languages.

Especially when they are close to something important to me.

Why am I telling you this? Because this text – and those that will follow, I hope - is a pure product of the social networking era and of our needless need to overshare ourselves: I'm going to write about my path towards mastering one language in particular, Wallon.

I'm not going to pull out information from Wikipedia (which, even in English, is very interesting by the way! : Walloon language).

But it's important for me to say that in the last century (1900 onwards), Walloon was virtually forbidden to be spoken in schools and other public places. The result: in less than a century, almost the entire population no longer knows how to speak Walloon.

My grandmother spoke Wallon, I can't.

I didn't like it.

So, instead of passively accepting the fact that the language of my ancestors was near to be no longer spoken, I decided to learn to write and speak it.

And to force myself to keep to this commitment, this learning, every week I'm going to make a summary of what I've done on the subject during that week (texts, words, grammar, etc...). I'm doing it on my own, not because I'm lazy, but because I'm unable, at the moment, to go to the few small evening classes available in Belgium (time constraints, geography...).

Anyway, that was the intro. For the next few, it won't be there. Welcome to my adventure! :D

Learning

Because I'm self-taught, I learn by writing different sentences. But I also use a very interesting website which is a goldmine for self-taught learners: Beljike.be

Well, so far I've really been concentrating on les noms

Exercises and case studies

I'm sure there are mistakes, as I'm just starting out, but you have to start somewhere. Compilation of what I've tried to do so far:

• Fanfic Title : Li song dau Koltira which mean “Koltira's blood”.

• A small texte in the above fanfic :

Èt vos m’ dimandez, m’ Madame dè l’ Mwârt, vos m’ dimandez qu’ dji frè po l’ rawè d’ novia à mès costés ? Dji vos dîrè « tortos ». Nin pace qu’ dj’ a nin tûsé èt dji sèprè nin dîre au jusse lès limites do c’qu’ dji so capâbe. Dji vos dît « tortos », pace qui v’s èstiz là, quand dji v’s a ôrdonè dins l’ timps do l’ rilachî èt ci maugré tot. Dji vos dît « tortos », pace qu’ sins li dji so rin, li qui èst m’-y âme, mi-y song, èt li rèstant d’ l’ umânité qui dj’a nin pus.

Which translate to :

And you ask me, My Deathlady, you ask me what I would do to have him back by my side ? I would tell you « everything ». Not because I haven't thought about it, and I wouldn't know the limits of what I'm capable of. I say « everything », because you were there when I ordered you to go and free him against all odds. I say « everything », because without him, I am nothing, he who is my soul, my blood, and the rest of humanity that I no longer have.

• A ficlet Fanfic : Lès biscwîts sont l' vîye : fanprompt do 4 awous' (Biscuit)

END

So there you have it, I hope to do more translating and writing next time because it's pretty empty right now...

Thanks for reading! :D