Data Science and Analytics Using Python
Master Python for Data Science: From Data Cleaning to Predictive Analytics
Skills you will gain:
About Program:
This three-day course covers essential data science techniques using Python, from data cleaning and EDA to advanced data visualization and predictive analytics. Participants will gain hands-on experience with Python libraries such as Matplotlib and Seaborn and will develop predictive models to analyze business data effectively.
Aim: To equip PhD scholars and academicians with advanced skills in data science and analytics using Python. This course focuses on data preprocessing, exploratory data analysis (EDA), data visualization, and predictive analytics, culminating in a capstone project that applies these skills to solve real-world business problems.
Program Objectives:
- Master data preprocessing and cleaning techniques.
- Perform exploratory data analysis using Python.
- Create advanced visualizations to communicate data insights.
- Develop predictive models to solve business problems.
- Apply data science techniques to real-world datasets.
What you will learn?
Day 1: Data Preprocessing and Exploratory Data Analysis (EDA)
- Data Cleaning Techniques
- Handling missing data, outliers, and data transformation
- Using Python libraries like Pandas for data manipulation
- EDA with Python
- Techniques for summarizing and visualizing data
- Identifying trends, patterns, and anomalies in datasets
Day 2: Data Visualization Tools (Matplotlib, Seaborn)
- Visualizing Data Insights
- Creating informative and aesthetically pleasing visualizations
- Using Matplotlib and Seaborn for various types of plots
- Hands-on Visualizations
- Interactive sessions creating visual reports
- Best practices for data storytelling with visualizations
Day 3: Predictive Analytics with Machine Learning
- Introduction to Predictive Modeling
- Supervised learning techniques (e.g., regression, classification)
- Building and evaluating predictive models with Python
- Capstone Project: Solving a Business Problem with Data
- Applying all learned skills to a real-world business problem
- Presenting the solution with actionable insights
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
Data scientists, analysts, researchers, and academicians in data science and related fields.
Career Supporting Skills
Program Outcomes
- Proficiency in Python for data preprocessing and exploratory analysis.
- Ability to create advanced data visualizations using Matplotlib and Seaborn.
- Competence in building and evaluating predictive models with Python.
- Hands-on experience in solving real-world business problems with data science.
- Enhanced skills in communicating data insights effectively.