The Data Science Essentials course is designed to equip you with the core skills required in today’s data-driven world. Through hands-on training, real-time projects, and expert-led sessions, you will learn how to collect, manage, analyze, and visualize data using the latest tools & technologies.
Whether you want to build applications, work with large datasets, or enter the analytics domain, this course prepares you for a strong career in tech.
What is Data Science & Analytics: overview, use-cases, industry relevance
Overview of data lifecycle: collection → cleaning → analysis → visualization → interpretation
Tools & environment setup (Python, R, Jupyter / IDEs, DB/SQL client)
Basic prerequisites — expectations, how to get started
Python Basics: syntax, data types, variables, operators, strings, lists/tuples/dictionaries, control flow (if/else, loops), functions, file I/O Codegnan+1
R Basics & statistical computing (if R is included as alternative to Python) — fundamentals and when to choose R vs Python for analytics
Working with data in Python: using libraries like Pandas, NumPy for data structures and array/data-frame manipulations eldalab.in+1
Importing/exporting datasets (CSV, Excel, JSON, SQL, etc.) Shiksha+1
Data cleaning & preprocessing: handling missing values, formatting, normalization, encoding/categorical handling
Descriptive statistics (mean, median, mode, variance, standard deviation)
Data summarization and grouping (aggregations, grouping, pivot-like operations)
Data visualization using libraries (e.g. matplotlib, seaborn for Python) — charts, histograms, scatter plots, correlation plots
Basic correlation & relationship analysis, handling distributions
Basic probability, distributions, sampling & sampling distributions
Hypothesis testing, t-tests, chi-square tests, ANOVA (as relevant)
Statistical reasoning for analytics: when and how to apply tests, interpreting results
Handling messy/real datasets: data cleaning, preprocessing, dealing with missing or inconsistent data
End-to-end mini-projects (e.g. dataset from finance/retail/health/business) — from data ingestion → cleaning → analysis → visualization → insights & reporting
Build a “portfolio project” as part of course assessment (capstone)
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