Next cohort begins on 1st September 2025 | Submit your application today | Top 40 applications will be accepted for next cohort | Best 3 candidates in each Cohort gets 100% sponsored mentorship.
Python for Data Science
Transform your career in just 10 weeks with Python for Data Science Program
Limited Batch
Live Data
Real Client
On Job Experience
Who Can Benfit From The Program?
Business Intelligence Skills Using Power BI Can Benefit A Wide Range Of Individuals And Organizations Including:
Students: Aiming a career in Data Science
Working Professional: Wants to transform over to Data Science Domain.
Freelancer: Looking for opportunities to upskill and network in Analytics Domain
Human Resource Professionals: Who want to analyze employee data to identify trends and opportunities for improvement.
Finance Professionals: Who want to track and analyze financial data to make better business decisions.
Business Managers: For intelligent decision making in their operations.
Data Analysts: Who are upskilling their business analytical skills.
Enrepreneurs & C-Level Executives: Wanting to scale their business using BI strategies.
Sales Professionals: Who want to analyze sales data to identify trends and opportunities.
Marketing Professionals: Who want to track and analyze marketing data to improve campaign performance.
Introduction to Python for Data Science
Overview of Python: Why Python is popular in data science.
Python Ecosystem: Key libraries and tools for data analysis.
Python Basics
Core Syntax: Variables, data types, and control structures.
Functions and Modules: Writing reusable code and importing libraries.
Data Structures in Python
Built-in Data Types: Lists, dictionaries, tuples, and sets.
Numpy Arrays: Working with multidimensional arrays for numerical data.
Data Manipulation with pandas
DataFrames and Series: Handling tabular data efficiently.
Data Cleaning: Filtering, sorting, and transforming data.
Data Visualization
Matplotlib and Seaborn: Creating static and interactive visualizations.
Plotting Techniques: Histograms, scatter plots, and heatmaps.
Exploratory Data Analysis (EDA)
Descriptive Statistics: Summarizing data with mean, median, and mode.
Visualization for EDA: Identifying patterns and outliers in data.
Working with CSV and Excel Files
Importing Data: Reading CSV, Excel, and other data formats into Python.
Data Export: Saving processed data back to CSV or Excel formats.
Introduction to Machine Learning with scikit-learn
Key Concepts: Supervised vs. unsupervised learning.
Basic Algorithms: Implementing linear regression and classification models.
Feature Engineering
Creating New Features: From existing data to improve model performance.
Handling Missing Data: Techniques like imputation and data interpolation.
Data Science Project Workflow
End-to-End Workflow: From data collection to model deployment.
Best Practices: Version control, documentation, and code organization.
Program Certificate
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Program Certificate
Meet Our Experts
VINAY BORHADE
Founder | Chief Data Scientist
MOHIT JAIN
PM | Data Scientist
AKASH POL
AI Mentor
How It Works?
1.Application Process
Apply for Program via dedicated link to show your interest
