Data Science & Analytics Full Course 2026: Statistics to Machine Learning — Complete Free Series (Updated August 2026)
Data Science and Analytics is the most-recruited advanced technology role in India in 2026. With NASSCOM projecting 11.9 million data-economy jobs by 2026 and the RBI mandating data governance frameworks for all scheduled banks, companies across every sector are hiring data analysts and scientists. What the job actually requires: statistical thinking to identify patterns, Python coding to wrangle large datasets, machine learning models to automate predictions, and business intelligence dashboards to communicate findings. ABC Trainings has produced a comprehensive 15-episode free series that covers the complete data science workflow — from statistical foundations to deploying machine learning models and NLP applications. This guide documents all 15 episodes so you know exactly what you will learn.
- 15 free episodes covering the complete data science curriculum: statistics → Python analytics → EDA → ML → NLP → time series → data careers
- Each episode is a self-contained module covering one major data science domain — structured for both complete beginners and career-switchers
- Prerequisites: basic Python syntax (variables, loops, functions) — the series assumes this but does not teach it
- Data analysts and scientists in India earn ₹5–25 LPA (freshers to 5+ years in Pune/Mumbai/Bangalore/Hyderabad)
- ABC Trainings' AI Powered Application Development workshop covers data science with Python in a structured mentored format
What Is Data Science vs Data Analytics? Updated August 2026
Data Science is the end-to-end practice of extracting business value from data — from collecting and cleaning raw datasets to building predictive models and deploying them in production. Data Analytics is a subset: it focuses on the descriptive and diagnostic analysis (what happened, why did it happen) using statistical methods and BI tools. The skill overlap is large: both require Python (Pandas, NumPy), statistical fluency (distributions, hypothesis testing), data visualisation (Matplotlib, Power BI), and SQL for database access. Data Scientists additionally build machine learning models (Scikit-learn, TensorFlow, PyTorch) and work closer to production ML pipelines. In India in 2026, the job title distinctions are blurry — most 'Data Analyst' JDs include basic ML requirements, and most 'Data Scientist' JDs require BI dashboard skills.
Full Series Overview — All 15 Episodes
The 15-episode series covers the complete data science journey.
| Episode | Topic | Key Skills |
|---|---|---|
| 01 | Introduction to Data Analytics | Data types, analytics mindset, business context |
| 02–03 | Statistics & Data Management | Descriptive stats, distributions, data quality |
| 04–05 | EDA & Visualisation | Pandas profiling, Matplotlib, Seaborn, storytelling |
| 06–08 | Python & Advanced Data Management | NumPy, Pandas, SQL integration, Power BI |
| 09–10 | Machine Learning (Intro & Advanced) | Regression, classification, clustering, Scikit-learn |
| 11–15 | NLP, Time Series, Ethics & Career | Text analysis, forecasting, GDPR/data ethics, portfolio |
Episode-by-Episode Breakdown: What Each Module Teaches
How to use this series most effectively: Episode 01 sets the mindset — watch it to understand what data analytics problems actually look like in business (customer churn, demand forecasting, fraud detection). Episode 02 (statistics) is the academic foundation — mean, median, standard deviation, distributions, hypothesis testing. Do not skip this even if it feels dry; statistics is what separates a data scientist from someone who just runs model.fit(). Episodes 04–05 (EDA and visualisation) are where you first make data tell a story — practice with a real dataset from Kaggle. Episodes 06–07 (Python analytics) are hands-on: NumPy arrays, Pandas DataFrames, groupby, merge, apply. Episode 09 (ML introduction) is the pivotal moment: when you train your first model (linear regression or decision tree) on a real dataset and see it predict outcomes on unseen data, the entire discipline clicks. Episodes 10–12 (advanced ML, NLP) build on that foundation. Episode 13 (Data Ethics) is increasingly relevant — every major Indian IT company has data governance frameworks, and knowing these principles helps in interviews.
Data Science Salary Guide — India 2026 (NASSCOM/Glassdoor Data)
Data Science / Analytics salaries in India (NASSCOM/Glassdoor 2026): Data Analyst (0–2 yrs, Python + SQL + BI tools) ₹5–8 LPA, Data Scientist (2–4 yrs, ML models + Python) ₹10–18 LPA, Senior Data Scientist (4–7 yrs, ML production systems) ₹18–30 LPA, Lead Data Scientist / ML Engineer (7+ yrs) ₹30–50+ LPA. Pune specifically: the Hinjewadi and Kharadi tech hubs have a growing data science cluster driven by analytics centres of Persistent Systems, Accenture, KPMG India, and 100+ AI startups. Fresh data analysts with a strong Python + SQL portfolio are getting interview calls within 2 weeks of applying at companies like LatentView Analytics, Quantiphi, and Tiger Analytics (Pune presence).
Data Science vs AI/ML vs Data Engineering — Which Path in 2026?
Data Science, AI/ML Engineering, and Data Engineering are three distinct but related paths. Data Science = analysis + modelling + business communication. AI/ML Engineering = productionising ML models (MLOps, model serving, monitoring) — stronger focus on software engineering than statistics. Data Engineering = building the pipelines that move, store, and prepare data (Apache Spark, Kafka, Airflow, cloud data lakes) — no ML required. For 2026 freshers choosing a path: Data Analytics is the lowest barrier to entry (Excel + Python + SQL gets first jobs); Data Science is the medium-term goal (ML + Python + statistics); AI/ML Engineering and Data Engineering are senior specialisations. Start with Data Analytics, build Python proficiency, then pivot to whichever branch your first role opens.
Companies Hiring Data Scientists in Pune & Maharashtra 2026
Pune companies with data science/analytics roles in 2026: Persistent Systems (Hinjewadi — AI & Analytics practice), Accenture Analytics (Pune), LatentView Analytics (Pune office), KPMG Advisory (Pune — data & analytics consulting), Quantiphi (Pune — AI products for banking), Bajaj Finance (Pune — credit risk analytics), HDFC Bank (Pune — fraud analytics), Infosys AI Analytics (Hinjewadi), TCS Research & Innovation (Pune — ML team), and 200+ data startups in Baner and Kharadi. Sambhajinagar: AURIC's industrial IoT data analytics centres opening in 2026–27 for manufacturing companies (Skoda, Toyota, Hyosung).
Learn Data Science at ABC Trainings
ABC Trainings' AI Powered Application Development workshop covers Data Science — Python, Pandas, EDA, Scikit-learn, and business analytics — in a structured 4–6 month program. Batches at Wagholi (Pune), Hadapsar (Pune), Cidco and Osmanpura (Sambhajinagar), and Sangli. Call 7039169629 or WhatsApp 7774002496. CMYKPY and PMKVY 4.0 fee subsidy — ask at enrollment.
Get the AI Powered Application Development Brochure + Fees + Batch Dates on WhatsApp
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💬 Get Brochure on WhatsApp📞 Call 7039169629About the author: Amit Kulkarni. 8 yrs leading IT training at ABC Trainings, ex-Infosys.
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FAQs
Do I need a mathematics or statistics background for data science?
A strong maths background helps but is not a prerequisite for getting started. You need: (1) basic algebra (equations, percentages, ratios — things learned in Class 10); (2) willingness to learn statistics concepts (Episode 02 of this series teaches them from scratch). Calculus and linear algebra become important if you go deep into neural networks, but for data analytics and standard ML (linear regression, decision trees, random forests), Class 12 mathematics is sufficient. The most important skill is honestly Python programming — invest there first.
What Python libraries do I need for data science in India?
The essential Python data science stack in 2026: NumPy (numerical computations), Pandas (data manipulation — the most-used library in real analytics work), Matplotlib + Seaborn (data visualisation), Scikit-learn (machine learning — 90% of ML interview questions involve Scikit-learn), and SQL via SQLAlchemy or Pandas read_sql. For advanced work: TensorFlow or PyTorch for deep learning, NLTK/SpaCy for NLP, statsmodels for time series analysis. Start with NumPy, Pandas, Matplotlib, and Scikit-learn — these four cover 80% of data analyst day-job tasks.
How is data science different from AI and machine learning?
Data Science is the broader discipline that includes everything from data collection to model deployment. Machine Learning is the core technology within data science — the algorithms that learn patterns from data (linear regression, decision trees, neural networks). AI (Artificial Intelligence) is the umbrella term for systems that mimic human intelligence, of which ML is the primary modern implementation. In practice: a data scientist builds ML models. An AI engineer productionises those models. The distinction matters for job-title targeting, but the skills overlap heavily.
Does ABC Trainings offer data science training with placement support in Pune?
Yes — ABC Trainings' AI Powered Application Development workshop includes Data Science with Python, covering all the skills needed for data analyst and data scientist roles. Batches at Wagholi, Hadapsar (Pune), Cidco, and Osmanpura (Sambhajinagar). Call 7039169629 or WhatsApp 7774002496 for batch dates and placement track record.
