Data Analytics using Python
The Data Analytics Using Python course is structured to take you step by step through the complete data analytics process. From learning the basics of Python and handling raw datasets to applying advanced visualization techniques, each module builds your skills through practical, real-world examples. The detailed syllabus is listed below.
Introduction to Data Analytics and Python
What is Data Analytics? Importance & Applications. Python for Data Analytics: Why Python? Setting up Python (Anaconda/Jupyter/VS Code). Python basics: variables, datatypes, operators, control structures
Data Preprocessing
Data types & data structures (lists, tuples, dictionaries, sets). Importing/exporting data (CSV, Excel, JSON, SQL). Handling missing data, Data type conversions, Data cleaning using Pandas.
Exploratory Data Analysis (EDA)
Introduction to NumPy & Pandas. Descriptive statistics (mean, median, variance, correlation). Data summarization, grouping, aggregation. Visualization with Matplotlib & Seaborn. Histograms, Boxplots, Scatter plots, Heatmaps.
DataBase & SQL Integration
Introduction to SQL for Data Analytics. Connecting Python with SQL (SQLite/MySQL).<br /> Running queries directly from Python. Combining SQL with Pandas.
Data Wrangling & Transformation
Filtering, sorting, merging, concatenating datasets. Handling outliers. Working with time-series data. String operations & text data cleaning.
Data Visualization
Advanced visualizations in Seaborn & Matplotlib. Data storytelling techniques followed by a<br /> case study.
Real-World Projects
Sales Data Analysis – Cleaning, EDA, and Visualization. Customer Segmentation, Stock Market Analysis – Time-series analytics with Pandas, Healthcare Data – Predictive analytics
Course Duration
12 Weeks
Capstone Project
End-to-end analytics project. Data cleaning → EDA → Visualization → Insights. Preparing analytics reports & presentations
Course Fee for group classes
INR 25,000 (Python+SQL+DA)