Overview
Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).
This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.
Our Trainers
Nikita Choudhary
She is a Trainer in TechSim .
Modules
Day 1: Introduction to Data Science & Python Basics
- What is Data Science? Applications & Lifecycle
- Setting up the environment (Anaconda/Jupyter Notebook)
- Python basics: variables, data types, operators
- Control structures: if, for, while
- Functions and Modules
- Mini Activity: Write Python functions for basic math/statistics
Day 2: Python Data Structures & File Handling
- Lists, Tuples, Sets, Dictionaries
- List comprehensions and lambda functions
- Reading & writing files (CSV, TXT)
- Exception Handling
- Hands-on: Read a file and generate summary statistics
Day 3: Introduction to NumPy
- Why use NumPy?
- Creating arrays, indexing, slicing
- Array operations and broadcasting
- Aggregations, statistical methods
- Hands-on: Perform vectorized operations and statistical analysis using NumPy
Day 4: Data Manipulation with Pandas (Part 1)
- Introduction to Pandas Series and DataFrame
- Reading data from CSV/Excel/JSON
- DataFrame indexing and selection
- Descriptive statistics and basic functions
- Hands-on: Load a real dataset and explore structure and summary
Day 5: Data Manipulation with Pandas (Part 2)
- Data Cleaning:
- - Handling missing values
- - Filtering and sorting
- - String operations
- GroupBy, aggregation, and pivot tables
- Merging and joining DataFrames
- Hands-on: Data preprocessing and aggregation project
Day 6: Data Visualization with Matplotlib
- Introduction to data visualization
- Line plots, bar charts, histograms, scatter plots
- Customizing plots: labels, colors, legends
- Subplots and multi-plot layouts
- Hands-on: Visualize patterns in a dataset using Matplotlib
Day 7 - 8: Interactive Visualizations with Plotly
- Introduction to Plotly (vs Matplotlib/Seaborn)
- Line charts, bar charts, scatter, pie charts
- Interactive dashboard elements
- Plotly Express vs Graph Objects
- Hands-on: Create interactive dashboards and charts
Day 9: Real-World Data Analysis Project
- Choose a dataset (e.g., Titanic, COVID-19, sales data)
- Apply:
- - Cleaning
- - Manipulation
- - Visualization
- Create meaningful insights using Pandas and visual libraries
- Final output: Jupyter Notebook report or dashboard
Day 10: Final Project Presentation + Bonus Topics
- Students present their final data analysis project
- Feedback and improvements
- Bonus Topics (as time permits):
- - Introduction to Machine Learning with Scikit-learn
- - Exploratory Data Analysis (EDA) techniques
- - Resources for further learning (Kaggle, books, MOOCs)
Day 1: Introduction to Data Science & Python Basics
- What is Data Science? Applications & Lifecycle
- Setting up the environment (Anaconda/Jupyter Notebook)
- Python basics: variables, data types, operators
- Control structures: if, for, while
- Functions and Modules
- Mini Activity: Write Python functions for basic math/statistics
-
Day 2: Python Data Structures & File Handling
- Lists, Tuples, Sets, Dictionaries
- List comprehensions and lambda functions
- Reading & writing files (CSV, TXT)
- Exception Handling
- Hands-on: Read a file and generate summary statistics
-
Day 3: Introduction to NumPy
- Why use NumPy?
- Creating arrays, indexing, slicing
- Array operations and broadcasting
- Aggregations, statistical methods
- Hands-on: Perform vectorized operations and statistical analysis using NumPy
-
Day 4: Data Manipulation with Pandas (Part 1)
- Introduction to Pandas Series and DataFrame
- Reading data from CSV/Excel/JSON
- DataFrame indexing and selection
- Descriptive statistics and basic functions
- Hands-on: Load a real dataset and explore structure and summary
-
Day 5: Data Manipulation with Pandas (Part 2)
- Data Cleaning:
- - Handling missing values
- - Filtering and sorting
- - String operations
- GroupBy, aggregation, and pivot tables
- Merging and joining DataFrames
- Hands-on: Data preprocessing and aggregation project
-
Day 6: Data Visualization with Matplotlib
- Introduction to data visualization
- Line plots, bar charts, histograms, scatter plots
- Customizing plots: labels, colors, legends
- Subplots and multi-plot layouts
- Hands-on: Visualize patterns in a dataset using Matplotlib
-
Day 7 - 8: Interactive Visualizations with Plotly
- Introduction to Plotly (vs Matplotlib/Seaborn)
- Line charts, bar charts, scatter, pie charts
- Interactive dashboard elements
- Plotly Express vs Graph Objects
- Hands-on: Create interactive dashboards and charts
-
Day 9: Real-World Data Analysis Project
- Choose a dataset (e.g., Titanic, COVID-19, sales data)
- Apply:
- - Cleaning
- - Manipulation
- - Visualization
- Create meaningful insights using Pandas and visual libraries
- Final output: Jupyter Notebook report or dashboard
-
Day 10: Final Project Presentation + Bonus Topics
- Students present their final data analysis project
- Feedback and improvements
- Bonus Topics (as time permits):
- - Introduction to Machine Learning with Scikit-learn
- - Exploratory Data Analysis (EDA) techniques
- - Resources for further learning (Kaggle, books, MOOCs)
Outcomes
-
Use data analysis tools in the pandas library
-
Load, clean, transform, merge and reshape data.
-
Handle external files as well as exceptions.
-
Analyze and manipulate time series data.
-
Solve real world data analysis problems.
Contact
Name: Khushboo Verma
Mobile No: 9301380563
