Data Analyst using Python
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Data Analyst using Python
Prerequisites
Learning Objectives
Course Overview
Module 1: Introduction to Data Analytics and Python
- Types of Data Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
- Data Analytics Lifecycle
- Real-world Applications
Overview, PVM, Installation, IDEs, First Program, I/O, Tokens, Variables
Data Types, Type Casting, Operators, Expressions
Conditional (if, else, elif), Loops (for, while)
Strings, Indexing, Slicing, Methods, Regular Expressions
Lists, Indexing, Slicing, Methods, Nested Lists, Comprehensions
Tuples, Sets : Creation, Indexing, Methods
Dictionaries: Access, Methods, Ordered Dicts
Defining Functions, Parameters, Lambdas, Modules, Recursion
File Operations, Text/Binary, pickle, csv, Random Access
Connect, CRUD Operations, Python with MySql/MongoDB
Module 2: Data Manipulation using Pandas
- Installing and Importing Pandas
- DataFrames and Series
- Loading Data from CSV, Excel, JSON
- Data Inspection: Head, Tail, Describe, Info
- Indexing and Slicing DataFrames
- Filtering and Sorting Data
- Handling Missing Data (Imputation Techniques)
- Grouping and Aggregation
- Merging, Joining, and Concatenating DataFrames
Module 3: Data Visualization using Matplotlib and Seaborn
Importance of Visualization in Analytics
- Basic Plots: Line, Bar, Histogram, Scatter, Pie
- Customizing Plots (Labels, Titles, Legends)
- Subplots and Multi-figure Plots
- Advanced Visualizations: Pairplot, Heatmap, Violin Plot, Box Plot
Module 4: Statistical Data Analysis using NumPy and SciPy
- Array Creation and Operations
- Mathematical and Statistical Functions
- Random Number Generation
- Statistical Distributions and Tests
- Hypothesis Testing (t-test, chi-square test)
- Correlation and Regression Analysis
- Optimization Technique
Module 5: Exploratory Data Analysis (EDA)
- Univariate, Bivariate, and Multivariate Analysis
- Detecting Outliers and Anomalies
- Feature Engineering and Transformation
- Distribution Plots, Correlation Heatmaps
- Pair Plots, Box Plots, Violin Plots
Module 6: Data Preprocessing and Feature Engineering
- Data Normalization and Standardization
- Handling Missing Values and Outliers
- Encoding Categorical Data (One-Hot Encoding, Label Encoding)
- Feature Scaling (Min-Max, Standard Scaler)
- Correlation-based Selection
- Chi-square, ANOVA, Mutual Information
- Principal Component Analysis (PCA)
- Feature Extraction
Module 7: Machine Learning with Scikit-learn
Supervised vs. Unsupervised Learning
- Model Selection and Training
- Train-Test Split, Cross-Validation
- Regression Models (Linear, Ridge, Lasso)
- Classification Models (Logistic Regression, KNN, SVM, Decision Trees)
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
Accuracy, Precision, Recall, F1-Score, AUC-ROC
Module 8: Time Series Analysis
Characteristics of Time Series
- Resampling, Rolling, and Shifting
- Decomposition of Time Series (Trend, Seasonality, Residuals)
- RIMA, SARIMA Models
- Moving Average and Exponential Smoothing
Module 9: Data Wrangling with SQL and Python Integration
- Basic SQL Queries (Select, Join, Aggregate)
- Integrating SQL with Python (SQLAlchemy, Pandas)
- Extracting and Transforming Data from Databases
- Query Optimization and Best Practices
Module 10: Capstone Project
- Problem Definition and Dataset Selection
- Data Cleaning and EDA
- Feature Engineering and Model Building
- Model Evaluation and Interpretation
- Visualization of Insights and Reporting
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Data Analyst using Python
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Certificate awarded upon completion of the course.
Monthly Tests
Regular monthly test series for progress evaluation.
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Training modules aligned with the latest CBSE syllabus.
Frequently Asked Questions
It involves using Python for data manipulation, visualization, and analysis through essential libraries.
You’ll learn NumPy, Pandas, Matplotlib, and Seaborn.
Topics include data cleaning, exploratory data analysis, and basic machine learning.
The course typically lasts 3-4 months.
You’ll cover basic machine learning techniques in the syllabus.
Anyone interested in data analytics with a background in Python or statistics.
Familiarity with Excel or databases is helpful but not mandatory.
Data analyst, data scientist, or business intelligence roles.
Yes, the course focuses on hands-on skills for data-driven decisions.
