Python for Everyone Masterclass
Rs. 799/- Only!
Next Session: 16th – 17th December 2023 | 10AM – 2.30PM IST
Level: Beginner | Programming Language: Python 3
✓ Master Python Programming with hands-on Live Project and become proficient with algorithmic thinking
✓ harness essential data manipulation skills using Key Python Libraries of NumPy and Pandas
✓ create impactful data visualizations with Matplotlib and Seaborn, and present data-driven insights effectively and persuasively
✓ expand toolkit and expertise by briefly exploring R language and mastering command-line skills
✓ Prepare For Entry-Level Job Interviews for data science and python programming
✓ Receive A Certificate Of Completion
✓ BONUSES: 120 Python Exercises to prepare for data science interviews And Cheatsheet!
What you will learn
The Masterclass covers the field of programming in detail using the popular Python language from scratch, and walks the learner through the most popular Data Science libraries widely used in the industry for data wrangling, data cleaning and data visualization. R programming language and Bash command line are briefly introduced.
The pedagogy is hands-on with exercises designed after significant number of concepts have been covered, since programming is a practical skill. A live hands-on project is covered where learners will develop a project from scratch either individually, or as a group, and receive feedback.
Prerequisites: There are no prerequisites other than basic mathematical skills & willingness to learn Python programming.
1. introduction to programming and python
✓ Explore foundational concepts of programming, understand the importance of coding, and learn how to think algorithmically to solve problems using Python.
✓ Discover what Python is and its relevance in various fields, including data science, web development, automation, and more. Learn why Python is one of the most popular programming languages of the 21st century.
✓ Learn basic command-line skills in Bash for data manipulation, automation, and file management. Discover how the command line can enhance your data science workflow.
✓ Get a glimpse of the R language, which is widely used for statistical analysis and data science. Understand how R complements Python in data analysis workflows.
3. Data Manipulation and Analysis
✓ Introduction to NumPy including overview of NumPy and its importance in data manipulation,
creating NumPy arrays and basic operations, navigating multidimensional arrays and indexing
✓ Advanced NumPy Techniques such as broadcasting and element-wise operations; aggregation, statistical analysis, and random number generation; linear algebra operations with NumPy
✓ Data Manipulation with Pandas including introduction to Pandas Series and DataFrames; data indexing, slicing, and filtering; data cleaning and handling missing values; combining, merging, and reshaping datasets
✓ Advanced Pandas for Data Analysis including grouping and aggregation with Pandas, time series data analysis, handling and visualizing categorical data, and introduction to data visualization with Pandas
5. Live project with mentorship
✓ Project Introduction including introduction to the business problem, overview of dataset to be used
✓ Data preprocessing and cleaning including data loading using Pandas, data preprocessing such as handling missing values and outliers, data transformation and feature engineering with NumPy and Pandas
✓ Basic Exploratory data analysis (EDA) including statistical analysis and visualization using Pandas and Seaborn, and identifying patterns and correlations in the data
✓ Data Visualization and Insights including designing data visualizations with Matplotlib and Seaborn, creating informative and visually appealing plots, extracting actionable insights, and presenting findings
✓ Final Deliverables will include a Jupyter notebook containing code, data analysis and visualizations, upon which feedback would be provided
2. Basics of Python Programming
✓ Setting up Python, writing and executing your first Python program, learning Python's simple and readable syntax
✓ Variables, Data Types, and Operators including declaring and using variables in Python, exploring fundamental data types, performing basic operations and arithmetic with operators
✓ Control Flow and Functions including implementing conditional statements (if, elif, else), creating loops (for and while) for repetitive tasks, defining and calling functions for code modularity, debugging techniques and error handling
✓ Explore essential data structures in Python, such as lists, tuples, dictionaries, sets, list comprehensions and more. Learn how to choose the right data structure for various tasks and manipulate data effectively.
4. Data visualization
✓ Introduction to Data Visualization including understanding the importance of data visualization, introduction to Matplotlib and Seaborn, setting up a visualization environment, creating basic plots such as line charts, scatter plots, and bar plots
✓ Customizing Visualizations including exploring customization options such as colors, markers, and line styles, adding labels, titles, and annotations to plots, configuring plot aesthetics and styles in Seaborn, creating multi-panel visualizations for complex data
✓ Exploratory Data Visualization with Seaborn including creating distribution plots: histograms, kernel density estimates, and box plots, visualizing relationships between variables using scatter plots and pair plots, heatmaps for correlation analysis and categorical data plotting
✓ Advanced Visualization Techniques including creating 3D plots and interactive plots, geographic data visualization with basemaps, combining Matplotlib and Seaborn for custom visualizations
BONUS Materials
✓ Gain access to a collection of 120 Python exercises designed to help you prepare for data science interviews. These exercises cover a wide range of topics and scenarios commonly encountered in data science interviews.
✓ Receive a handy Python cheatsheet for quick reference. This cheatsheet provides key Python syntax and concepts to support your learning journey.
1. introduction to programming and python
✓ Explore foundational concepts of programming, understand the importance of coding, and learn how to think algorithmically to solve problems using Python.
✓ Discover what Python is and its relevance in various fields, including data science, web development, automation, and more. Learn why Python is one of the most popular programming languages of the 21st century.
✓ Learn basic command-line skills in Bash for data manipulation, automation, and file management. Discover how the command line can enhance your data science workflow.
✓ Get a glimpse of the R language, which is widely used for statistical analysis and data science. Understand how R complements Python in data analysis workflows.
2. Basics of Python Programming
✓ Setting up Python, writing and executing your first Python program, learning Python's simple and readable syntax
✓ Variables, Data Types, and Operators including declaring and using variables in Python, exploring fundamental data types, performing basic operations and arithmetic with operators
✓ Control Flow and Functions including implementing conditional statements (if, elif, else), creating loops (for and while) for repetitive tasks, defining and calling functions for code modularity, debugging techniques and error handling
✓ Explore essential data structures in Python, such as lists, tuples, dictionaries, sets, list comprehensions and more. Learn how to choose the right data structure for various tasks and manipulate data effectively.
3. Data Manipulation and Analysis
✓ Introduction to NumPy including overview of NumPy and its importance in data manipulation,
creating NumPy arrays and basic operations, navigating multidimensional arrays and indexing
✓ Advanced NumPy Techniques such as broadcasting and element-wise operations; aggregation, statistical analysis, and random number generation; linear algebra operations with NumPy
✓ Data Manipulation with Pandas including introduction to Pandas Series and DataFrames; data indexing, slicing, and filtering; data cleaning and handling missing values; combining, merging, and reshaping datasets
✓ Advanced Pandas for Data Analysis including grouping and aggregation with Pandas, time series data analysis, handling and visualizing categorical data, and introduction to data visualization with Pandas
4. Data visualization
✓ Introduction to Data Visualization including understanding the importance of data visualization, introduction to Matplotlib and Seaborn, setting up a visualization environment, creating basic plots such as line charts, scatter plots, and bar plots
✓ Customizing Visualizations including exploring customization options such as colors, markers, and line styles, adding labels, titles, and annotations to plots, configuring plot aesthetics and styles in Seaborn, creating multi-panel visualizations for complex data
✓ Exploratory Data Visualization with Seaborn including creating distribution plots: histograms, kernel density estimates, and box plots, visualizing relationships between variables using scatter plots and pair plots, heatmaps for correlation analysis and categorical data plotting
✓ Advanced Visualization Techniques including creating 3D plots and interactive plots, geographic data visualization with basemaps, combining Matplotlib and Seaborn for custom visualizations
5. Live project with mentorship
✓ Project Introduction including introduction to the business problem, overview of dataset to be used
✓ Data preprocessing and cleaning including data loading using Pandas, data preprocessing such as handling missing values and outliers, data transformation and feature engineering with NumPy and Pandas
✓ Basic Exploratory data analysis (EDA) including statistical analysis and visualization using Pandas and Seaborn, and identifying patterns and correlations in the data
✓ Data Visualization and Insights including designing data visualizations with Matplotlib and Seaborn, creating informative and visually appealing plots, extracting actionable insights, and presenting findings
✓ Final Deliverables will include a Jupyter notebook containing code, data analysis and visualizations, upon which feedback would be provided
BONUS Materials
✓ Gain access to a collection of 120 Python exercises designed to help you prepare for data science interviews. These exercises cover a wide range of topics and scenarios commonly encountered in data science interviews.
✓ Receive a handy Python cheatsheet for quick reference. This cheatsheet provides key Python syntax and concepts to support your learning journey.
Instructor
anant agarwal
Anant works as a Data Science Manager at a Fortune Global 500 company. An alumnus of The Doon School and IIT Kharagpur, he holds an MS from University of Minnesota Twin Cities where he received the Forrest Fellowship and MBA from Indian School of Business Hyderabad with Dean's and Merit List awards.
Anant has been featured as a Guest Speaker at Analytics Vidhya's DataHack Summit and DataHour sessions, Zhejiang University China and has been a Judge in Responsible AI category for Altair Global Enlighten Award.
He is also a 2-time 99.8 percentiler in CAT, National-level Squash player and a fingerstyle guitarist.
For any assistance or queries, please reach out to us at [email protected]
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