• Python files I/O Functions
• Strings and related operations
• Numbers
• Lists and related operations
• Tuples and related operations
• Dictionaries and related operations
• Sets and related operations

• Functions
• Variable Scope and Returning Values
• Function Parameters
• Global Variables
• Standard Libraries
• Modules Used in Python
• Lambda Functions
• Object Oriented Concepts
• The Import Statements
• Errors and Exception Handling
• Module Search Path
• Package Installation Ways
• Handling Multiple Exceptions

• Data Analysis
• Operations on arrays
• NumPy - arrays
• Indexing, slicing and iterating
• Pandas - data structures & index operations
• Reading and writing arrays on files
• Metadata for imported Datasets
• Matplotlib library
• Reading and Writing data from CSV/Excel formats into Pandas
• Grids, axes, plots
• Types of plots - bar graphs, pie charts, histograms
• Markers, colours, fonts and styling
• Contour plots

• Basic Functionalities of a data object
• Concatenation of data objects
• Merging of Data objects
• Exploring a Dataset
• Types of Joins on data objects
• Analyzing a dataset

• Python Revision 
• What is Machine Learning?
• Machine Learning Process Flow
• Machine Learning Use-Cases
• Machine Learning Categories
• Linear regression

• What are Classification and its use cases?
• Algorithm for Decision Tree Induction
• Confusion Matrix
• What is a Decision Tree?
• Creating a Perfect Decision Tree
• What is Random Forest?

• Introduction to Dimensionality
• PCA
• Why Dimensionality Reduction
• Scaling dimensional model
• Factor Analysis
• LDA

• What is Naïve Bayes?
• How Does Naïve Bayes Work?
• What is a Support Vector Machine?
• Implementing Naïve Bayes Classifier
• Implementation of Support Vector Machine for Classification
• Hyperparameter Optimization
• Illustrate how Support Vector Machine works
• Grid Search vs Random Search

• What are Clustering & its Various Use Cases?
• How K-means algorithm work?
• What is K-means Clustering?
• What is C-means Clustering?
• How does Hierarchical Clustering work?
• What is Hierarchical Clustering?
• How to do optimal clustering?

• What are Association Rules?
• Calculating Association Rule Parameters
• How do Recommendation Engines work?
• Recommendation Engines
• Association Rule Parameters
• Content-Based Filtering
• Collaborative Filtering

• What is Reinforcement Learning?
• Elements of Reinforcement Learning
• Why Reinforcement Learning?
• Epsilon Greedy Algorithm
• Q values and V values
• Exploration vs Exploitation dilemma
• Q – Learning
• Markov Decision Process (MDP)
• Values

• What is Time Series Analysis?
• Components of TSA
• Importance of TSA
• AR model
• White Noise
• MA model
• ARIMA model
• ARMA model
• Stationarity
• ACF & PACF

• What is Model Selection?
• Cross – Validation
• Need for Model Selection
• How do Boosting Algorithms work?
• What is Boosting?
• Adaptive Boosting
• Types of Boosting Algorithms

• What is Exploratory Data Analysis?
• EDA Classification
• EDA Techniques
• Univariate Graphical EDA
• Multivariate Non-graphical EDA
• Multivariate Graphical EDA
• Heat Maps

• Data Visualization
• Business Intelligence tools
• VizQL Technology
• Connect to data from the File
• Connect to data from the Database
• Basic Charts
• Chart Operations
• Combining Data
• Calculations

• Trend lines
• Reference lines
• Geographic Maps
• Forecasting
• Clustering
• Using charts effectively
• Visual best practices
• Dashboards
• Story Points
• Publish to Tableau Online

If you have prior coding experience, learning Python for Data Science will be easier. However, it is not compulsory.

• Knowledge of programming languages like R, SQL, and Python
• Knowledge of Statistics and other related concepts
• Machine learning for effectively handling big sets of data
• Data wrangling to refine data
• Knowledge of Linear Algebra and Multivariable Calculus
• Knowledge of data visualization tools for effortless communication of insights collected

• Business Analyst
• Big Data Engineer or Data Architect
• Database Administrator
• Business Intelligence (BI) Developer
• Data Analyst
• ML Engineer
• Data Scientist
• Business Intelligence Analyst
• Statistician
• Natural Language Processing Engineer
• Computer Vision(CV) Engineer
• MLOps Engineer

• BI Managers 
• Project Managers
• Software Developers 
• ETL Professionals
• Analytics Professionals
• Big Data Professionals

You don’t need prior knowledge for this Data Science with Python course. However, working knowledge of programming can help.