Start your journey for free
Begin your learning experience and become a data scientist with certificate courses curated to land your dream job.
Skills Covered in this Path
- Collection & preprocessing
- Statistical analysis
- Probability
- Data acquisition
- Supervised & unsupervised learning
- Feature engineering
- Model evaluation
- Classification
- Prediction
- Clustering
- R & Python analysis
- Data visualization
- Ethics & privacy
- Data Analytics
- Problem-solving
- Insights
- Predictive Modeling
- Business Intelligence
- Data Science Process
- Data Preprocessing Techniques
- Data Science Components
- Career Trajectory
- Programming Basics
- Data Handling using Python
- Numpy and Pandas
- 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
- Population
- Samples
- Statistical analysis
- Hypothesis testing
- Statistical distributions
- Business Intelligence Fundamentals
- Data Visualization Principles
- Introduction to Tableau
- Understanding Data Types
- Navigating the Tableau Interface
- Creating Dashboards
- Visual Analytics Techniques
- Hands-on Tableau Exercises
- Integrating Data Sources.
- Power BI usage
- data loading
- creating reports
- dashboards
- slicers & filters
- visual interactivity
- Practical visualization walkthrough
- IPL data analysis with Python
- Data Description
- Basic Data Understanding
- Data Manipulation
- Skewness Using Histograms & Density Curves
- Visualising Outliers Using Boxplots
- Visualising Correlation Using Scatter Plots/Heatmaps
- What Is Machine Learning
- Regression Analysis
- Linear Reg
- 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
- Model training & tuning
- Regularization
- Image processing (NNs)
- Feature & object detection
- Image classification
- CV problem-solving
- Pixel & image manipulation
- Flask
- Model Deployment
- Model Deployment
- Heroku
- Forecasting using Python
- Exponential Smoothing
- ARIMA
- Time Series in R
- R programming fundamentals
- variables
- data types
- data structures
- control structures
- functions
- packages
- importing data into R
- manipulating data in R
- performing statistical analysis in R
- data cleaning and wrangling
- statistical modeling
- Exploratory data analysis
- summary statistics
- data cleaning
- visualization (histograms
- boxplots
- scatter)
- handling missing values
- Marginal Probability
- Bayes Theorem
- Binomial Distribution
- Normal Distribution
- Poisson Distribution
- Descriptive statistics
- probability theory
- hypothesis testing
- regression analysis
- decision making methods
- Linear Regression
- Concept of Multicollinearity
- R Square
- Predictive Modeling
- Credit & market risk analysis
- counterparty risk management
- regulatory capital
- derivative valuation
- XVA
- risk identification
- hedging strategies
- and quantitative model validation
- 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
