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Data Science Classroom Program
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Course Description
Unlock the world of data science with our Data Science Classroom Program. Master Python, machine learning, and visualization to excel in data-driven industries. Elevate your career with skills that drive innovation and informed decision-making!
In case of any queries reach us via Call/Whatsapp on: +91-8920041051
Offline Locations
Course Overview
Our Data Science Classroom Program is completely Offline and designed to bridge the gap by equipping you with an understanding of Python, Data Analysis, Machine Learning, and Mathematics. We delve into the intricacies of Deep Learning, Natural Language Processing (NLP), and their wide-ranging applications in the modern AI industry. Our program goes beyond the technical aspects. We understand the daily challenges and pain points faced by data scientists, and we'll guide you in developing practical solutions.
Get ready to discover the magic of analyzing data, mastering machine learning, and predicting trends with expert guidance. This is your opportunity to dive deep into a field with endless career possibilities.
Key Highlights:
- 3 months of Offline Classes
- Live Doubt Sessions by Industry Experts
- Hands-On Learning: Learn by doing real projects for practical experience
- Practice Questions & Weekly Assignments
- Industry Experts: Learn from experienced professionals in the field
- Resume-building course as an add on.
Projects:
- Performing Exploratory data analysis on Airbnb data.
- Income Prediction based on its social and financial attributes supervised learning
- Market Basket Analysis unsupervised learning
- Working on Sentiment Analysis for understanding natural language processing
- Identifying hand-written numbers from images - Computer Vision
- Image and voice classification Deep learning model
By enrolling in our program, you won't just gain knowledge; you'll gain insight into the real-world scenarios that data scientists navigate daily. Join us in mastering the art and science of data and AI, and empower yourself to thrive in this digital era.
Course Content
Day 1: Introduction to Data Science and Machine Learning
- Overview of Data Science
- Fundamentals of Machine Learning
- Types of Machine Learning Algorithms
- Applications of Data Science and Machine Learning
- Introduction to Python programming language
- Setting up Python environment
- Running the first Python program
Day 2: Introduction to Python
- Basic syntax and data types in Python
- Working with variables and operators
- Using built-in functions and libraries
Day 3: Know more about Python
- Lists, tuples, and dictionaries
- Conditional statements and loops
- User-defined functions
- functions such as map, filter, lambda
Day 4: Class & OOPs Concepts
- Class & OOPs Concepts
Get recorded videos on Python for Data Science
Day 1: Data Cleaning and Preprocessing with Pandas
- Import and Exporting data
- Handling missing data
- Removing duplicates and dealing with Outliers
- Cleaning and adjustments in data
Day 2: Exploratory Data Analysis (EDA) with Pandas
- Descriptive Statistics and data summarization
- Grouping and Aggregating data
- SQL-like operation in data
Day 3: Data Visualization with Matplotlib
- Creating Basic Plots (line plots, scatter plots, histograms)
- Customizing and Styling Visualizations
- Creating Informative and Aesthetically Pleasing Visualizations
- Pair plots, Heatmaps, and Advanced Plotting technique
Day 4:Knowing about the data
- Types of analysis - univariate, multivariate analysis
- creating a hypothesis of project approach
- Introduction to statistical tests and hypothesis testing
- Implementing statistical tests in Python
Get recorded videos on Data Analysis with Python
Day 1: Final Project and Case Studies
- Participants work on a real-world data analysis project
- Applying learned Python skills to analyze and visualize data
Day 2: Feature Transformation
- Encoding
- Scaling
Day 3: Feature Reduction
- Feature Selection
- Dimetional Reduction
Day 4: Feature Balancing
- Smote, ENN, Random Sampling
Get recorded videos on Data Analysis with Python
Day 1: Probability and Statistics
- Basic Probability Concepts
- Random Variables
- Probability Distributions
- Statistical Inference
- Hypothesis Testing
- Regression Analysis
Day 2: Statistical Analysis with Python
- Measures of central tendency and dispersion
- Hypothesis testing and confidence intervals
- Correlation and regression analysis
- ANOVA and chi-square tests
Day 3: Supervised learning
- Classification
- Naive Bayes
Day 4: Supervised Learning
- KNN