Python with data science

Python Basics & Syntax

Core Python syntax, indentation rules, comments and writing clean readable code for data science workflows.

Functions & Data Structures

Defining functions, working with lists, tuples, sets and dictionaries for data manipulation.

Scientific Computing Introduction

Overview of the scientific Python ecosystem including NumPy, Pandas and Matplotlib.

Python Environment Setup

Installing Python, Jupyter Notebook and Anaconda. Configuring the data science development environment.

NumPy Arrays & Operations

Creating and manipulating NumPy arrays. Indexing, slicing and reshaping arrays for numerical computing.

Mathematical Functions

Using NumPy mathematical functions for linear algebra, statistics and array-wise computations.

Data Manipulation Techniques

Transforming and restructuring arrays using NumPy operations for efficient data processing.

Array Processing

Broadcasting, vectorized operations and performance optimization with NumPy arrays.

DataFrames & Series

Creating and working with Pandas DataFrames and Series. Indexing, selecting and filtering data.

Data Import & Export

Reading and writing CSV, Excel and JSON files using Pandas read and write functions.

Data Cleaning & Transformation

Renaming columns, changing data types, merging, joining and reshaping DataFrames.

Handling Missing Data

Detecting, filling and dropping missing values using Pandas isnull, fillna and dropna methods.

Matplotlib Implementation

Setting up Matplotlib, creating figures and axes and customizing plot styles and themes.

Line Charts, Bar Graphs & Pie Charts

Plotting line charts for trends, bar graphs for comparisons and pie charts for proportions.

Histograms & Scatter Plots

Visualizing data distributions with histograms and relationships between variables with scatter plots.

Custom Visualization Techniques

Adding titles, labels, legends, annotations and custom colors to create publication-ready plots.

Charts & Reporting Dashboards

Combining multiple plots into dashboards using subplots and gridspec layouts.

Business Insight Visualization

Creating visualizations that communicate business metrics and KPIs clearly to stakeholders.

Real-Time Graphical Representation

Plotting live and streaming data using Matplotlib animation and dynamic update techniques.

Seaborn for Statistical Graphs

Using Seaborn to create aesthetically pleasing statistical visualizations with minimal code.

Advanced Data Visualization Techniques

Pair plots, joint plots and FacetGrid for exploring multidimensional datasets visually.

Heatmaps & Distribution Plots

Visualizing correlation matrices with heatmaps and data distributions with KDE and violin plots.

Correlation & Regression Visualization

Plotting regression lines and correlation relationships between variables using Seaborn lmplot.

Categorical Data Visualization

Using bar plots, box plots, count plots and strip plots to visualize categorical variables.

Interactive Statistical Reporting

Building interactive charts and reports using Plotly and integrating them into Jupyter notebooks.

Real-Time Visualization Projects

Applying Seaborn and Matplotlib together on real datasets to generate insight-driven visual reports.

Data Interpretation

Reading and making sense of data summaries, distributions and visual patterns during EDA.

Correlation Analysis

Measuring relationships between variables using Pearson and Spearman correlation coefficients.

Trend Identification

Spotting trends, seasonality and anomalies in time series and tabular datasets.

Business Insights Generation

Translating data findings into actionable business recommendations and clear visual narratives.

Descriptive Statistics

Computing mean, median, mode, variance and standard deviation to summarize datasets.

Probability Concepts

Understanding probability distributions, random variables and the basics of Bayesian thinking.

Hypothesis Testing Basics

Performing t-tests, chi-square tests and interpreting p-values and confidence intervals.

Statistical Reporting

Documenting and presenting statistical findings clearly using tables, charts and written summaries.

Introduction to Machine Learning

Overview of supervised and unsupervised learning, key algorithms and the ML workflow.

Data Preparation

Feature selection, encoding categorical variables, scaling and splitting data into train and test sets.

Model Building Fundamentals

Training linear regression and classification models using Scikit-learn on real datasets.

Evaluation Metrics

Measuring model performance using accuracy, precision, recall, F1 score, MAE and RMSE.

Dataset Analysis Projects

End-to-end analysis of real-world datasets covering cleaning, EDA, visualization and reporting.

Business Case Studies

Solving industry problems using data science techniques across retail, finance and healthcare domains.

Data Reporting Techniques

Creating professional data reports and dashboards using Jupyter, Matplotlib and Pandas.

Practical Assessments

Hands-on assignments and mini projects to assess and reinforce data science skills learned throughout the course.