Data Analytics

<p>Dive into the World of Data Analytics with This 2025 Guide!</p><p>Hey there if you're looking to master data analytics from the ground up this book is your ultimate companion. It kicks off with the basics in Part I covering the data analytics landscape including modern lifecycles like CRISP-DM and agile workflows. You'll explore core data structures: structured unstructured and semi-structured. Learn about key roles like data analyst scientist and ML engineer. Get an intro to AI and generative AI in analytics. Move to statistical foundations: descriptive stats distributions inferential methods like hypothesis testing and confidence intervals. Dive into correlation linear regression and applied linear algebra. Then Python programming: setup Pandas for manipulation NumPy for computing Scikit-learn for modeling. Part II dives into the workflow: relational databases advanced SQL with joins window functions optimization. Data sourcing from enterprises APIs web scraping-including AI-powered and ethical aspects. Data prep: profiling deduplication missing data handling outliers transformations. Exploratory analysis: univariate bivariate multivariate with PCA communicating findings.</p><p>What sets this book apart is its 2025 focus-blending timeless foundations with cutting-edge trends like AI automation real-time streaming and cloud lakehouses that older books overlook. Unlike generic guides it packs real-time case studies like Uber's AI agents or Tesla's data structures plus job skill enhancements tied to market data showing ML engineer growth at +34%. It bridges gaps other texts miss like ethical scraping or sentiment analysis for brands with hands-on Python applications and career pathways. No fluff; it's practical updated for today's job market giving you a competitive edge in high-demand roles.</p><p>This author has no affiliation with the board and it is independently produced under nominative fair use.</p>