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Skills Covered in this Path
- Data Science
- Analytics Landscape
- Data Science Life Cycle fundamentals
- Programming Concepts
- Python Basics
- Variables and Data types in Python
- Operators and Strings in Python
- Python Data Structures
- Control Flow Statements and Functions
- OOPs
- Components of Data Science
- Data Science Architecture
- Skills needed to learn Python
- NumPy and Pandas libraries
- Python Basics
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Plotly
- Basics of Probability
- Marginal Probability
- Bayes Theorem
- Central Tendency
- Measures of Variability
- Measure of Skewness
- Kurtosis
- Hypothesis Testing
- T-test
- Probability
- Statistics
- Normal Distribution
- Sampling Distribution
- Hypothesis
- Central Limit Theorem
- Tableau
- Data Visualization
- Data Science
- Data Visualization
- Power BI
- Components of Power BI
- Visual Analytics
- Practical visualization walkthrough
- IPL data analysis with Python
- Visualising
- Regression Analysis
- Machine Learning
- Data Transformation
- Python
- Jupyter Notebook
- Statistics
- Regression Models
- Data Analytics
- Data Visualizations
- Unsupervised Learning
- Clustering
- k-means Clustering
- Covid Analysis
- Analysis of Indian Education System
- Project on FIFA Data
- Machine Learning
- Student grade prediction
- Salary prediction
- Predicting beer consumption
- ANN
- Tensorflow
- Keras
- Gradient
- Backpropagation
- Reinforcement Learning
- States
- Actions
- State based mechanism in Reinforcement Learning
- Data Augmentation
- Weight Initialization
- Regularization
- Image processing using Neural Networks
- Image Classification
- Case study problems
- Object Detection Using OpenCV and Python Converting Images to Different Forms"
- Flask
- Model Deployment
- Model Deployment
- Heroku
- Forecasting using Python
- Exponential Smoothing
- ARIMA
- Time Series in R
- R Commands
- R Packages
- R Functions
- R Datatypes
- Operators in R
- RStudio
- EDA concepts
- EDA in python
- Visualization tools
- Marginal Probability
- Bayes Theorem
- Binomial Distribution
- Normal Distribution
- Poisson Distribution
- Hypothesis Testing
- Type I and Type II error
- Chi-Square test
- ANOVA
- Linear Regression
- Concept of Multicollinearity
- R Square
- Predictive Modeling
- Credit Risk Modelling
- Market Risk Optimization
- RFM Analysis
- KINME
- Clustering
- KINME
- Linear Programming
- Data Science Architecture
- Components of Data Science
- Popular applications of Data Science
- Time series analysis
- Model forecast theory
- Time Series Forecasting
- Time Series Demo
- Feature Selection
- Linear Discriminant Analysis with Python
- Time series forecasting
- Clustering
- Market Basket Analysis
- Regression
- CART
- Random Forest
- Time Series Forecasting
- Decision Trees
- Credit Risk Modeling
