What You Will Learn
Understand core concepts of Data Analytics & Data Science and how data is used for business decisions.
Analyze and clean datasets using Excel, SQL, and Python for real-world scenarios.
Master SQL to extract, filter, join, and manage large datasets from relational databases.
Work with Python libraries like Pandas, NumPy, Matplotlib & Seaborn for analysis and visualization.
Perform Exploratory Data Analysis (EDA) to identify patterns, correlations, and insights.
Learn essential Statistics & Probability for analytical decision-making and A/B testing.
Build interactive dashboards using Power BI or Tableau for business reporting.
Apply Machine Learning basics (Regression, Classification, Clustering) on real datasets.
Create end-to-end data projects including data cleaning → analysis → visualization → ML modeling.
Develop industry-ready portfolios with multiple projects and prepare for Data Analyst/Data Science interviews.
About Course
This course equips learners with end-to-end skills required to collect, clean, analyze, visualize, and model data to solve real business problems. Students will work on practical datasets, create dashboards, build predictive models, and learn how to communicate insights clearly to stakeholders.
Course Curriculum
Data Analytics Foundations (The Core Layer)
- Introduction to Data Analytics & Data Science: Data roles, workflows, industry use-cases.
- Data Types & Structures: Structured vs unstructured data, files, rows, columns.
Data Collection Process: APIs, databases, CSV/Excel importing.
Excel & Spreadsheet Mastery
- Advanced Excel Skills: Formulas, pivot tables, lookups, text functions.
- Dashboard Creation: KPI dashboards, charts, slicers, interactive reports.
- Automation: Power Query basics & Excel data cleaning tools.
SQL for Data Analysis (RDBMS)
- Core SQL: SELECT, WHERE, GROUP BY, ORDER BY queries.
- Joins & Relationships: INNER, LEFT, RIGHT, FULL joins.
- Advanced SQL: Subqueries, functions, views, stored procedures.
- Database Design: Tables, keys, normalization
Python for Data Analytics
- Python Basics: Variables, loops, conditions, functions.
- Data Libraries: NumPy for calculations, Pandas for cleaning & manipulation.
- File Handling: CSV, Excel, JSON data processing.
- Automation Scripts: Real-time data processing tasks.
Data Cleaning & Preprocessing
- Missing Data Handling: Imputation techniques.
- Outliers & Noise: Detection and fixing methods.
- Feature Engineering: Encoding, scaling, transformation.
- Data Quality Checks: Validation & verification processes.
Exploratory Data Analysis (EDA)
- Statistical Summary: Mean, median, mode, distribution shape.
- Correlation & Patterns: Heatmaps, scatter analysis.
- Trend Analysis: Time series observation.
- Insight Writing: Finding hidden stories.
Statistics & Probability (For Data Science)
- Probability Basics: Events, conditional probability.
- Distributions: Normal, binomial, Poisson.
- Hypothesis Testing: p-value, t-test, chi-square test.
- A/B Testing: Business decision-making.
Data Visualization (BI Tools + Python)
- BI Dashboards: Power BI / Tableau interactive dashboards.
- Python Visualization: Matplotlib, Seaborn charts.
- Storytelling: Insight framing, presenting to stakeholders.
- Color & Chart Selection: Best practices for clarity.
Machine Learning – Supervised Learning
- Regression Models: Linear, Multiple, Polynomial Regression.
- Classification Models: Logistic, Decision Trees, Random Forest, SVM.
- Model Evaluation: Accuracy, precision, recall, ROC curve.
- End-to-End Pipeline: Splitting, training, testing, tuning.
Machine Learning – Unsupervised Learning
- Clustering: K-Means, Hierarchical Clustering.
- Dimensionality Reduction:
- Anomaly Detection: Real-time use cases.
- Segmentation: Customer grouping insights.
Big Data & Cloud Analytics (Overview)
- Hadoop Ecosystem: HDFS, MapReduce.
- Apache Spark Basics: RDDs, DataFrames, PySpark intro.
- ETL Pipeline: Extract, Transform, Load concepts.
- Cloud Tools Overview: AWS/Azure/Google analytics services.
Business Intelligence & Reporting
- KPI Design: How to choose correct metrics.
- Report Writing: Executive-level summaries.
- Presentation: How analysts explain findings.
- Case Study Discussions: Real industry examples.
Capstone Projects & Integration
- Real Dataset Projects: E-commerce, sales, HR analytics, finance datasets.
- End-to-End ML Project: Data → EDA → Model → Deployment concept.
- Portfolio Building: GitHub, resume mapping, LinkedIn optimization.
- Interview Preparation: Case study, SQL/Excel/Python practice.
Requirements
- •Basic understanding of computer operations.
- •No prior coding or analytics experience required (Beginner to Advanced).
- •A laptop with at least 8GB RAM (recommended 16GB for machine learning workloads).
- •Stable internet connection for accessing datasets, dashboards, and cloud tools.
- •Basic English communication skills for understanding analytics terminology.
Material Includes
- •Source Code & Notebooks for all Python, SQL, and Machine Learning projects.
- •Data Analytics & Data Science Cheat Sheets (Python, Pandas, NumPy, Statistics, SQL).
- •Practice Datasets (Finance, HR, E-commerce, Healthcare, Marketing, Sales).
- •E-books on Data Analytics, BI Dashboards, and Machine Learning.
- •Access to our Exclusive Data Analyst Community (Doubt-solving + Career guidance).
- •Capstone Project Templates for GitHub portfolio building.
- •Power BI & Tableau Practice Files for visualization training.
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