Data Analytics
From Foundation to Specialization (2025 Edition)
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Dive into the World of Data Analytics with This 2025 Guide! 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 met...
Dive into the World of Data Analytics with This 2025 Guide! 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. 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. This author has no affiliation with the board and it is independently produced under nominative fair use.
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