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### How to Begin and Enroll for Data Science Courses easily?

- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- K-Means Clustering

- Successfully perform all steps in a complex Data Science project
- Create Basic Tableau Visualisations
- Perform Data Mining in Tableau
- Understand how to apply the Chi-Squared statistical test
- Apply the Ordinary Least Squares method to Create Linear Regressions
- Assess R-Squared for all types of models
- Assess the Adjusted R-Squared for all types of models
- Create a Simple Linear Regression (SLR)

- How to clean and prepare your data for analysis
- How to perform basic visualization of your data
- How to model your data
- How to curve-fit your data
- And finally, how to present your findings and wow the audience

- Build artificial neural networks with Tensorflow and Keras
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Classify images, data, and sentiments using deep learning
- Implement machine learning at massive scale with Apache Spark's MLLib
- Understand reinforcement learning - and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross-validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values

- Program in R
- Use R for Data Analysis
- Create Data Visualizations
- Use R to handle CSV, Excel, SQL files or web scraping
- Use R to manipulate data easily
- Use R for Machine Learning Algorithms
- Use R for Data Science

- Understand the fundamentals of statistics
- Learn how to work with different types of data
- How to plot different types of data
- Calculate the measures of central tendency, asymmetry, and variability
- Calculate correlation and covariance
- Distinguish and work with different types of distributions
- Estimate confidence intervals
- Perform hypothesis testing
- Make data-driven decisions
- Understand the mechanics of regression analysis
- Carry out regression analysis
- Use and understand dummy variables
- Understand the concepts needed for data science even with Python and R!

- Create and use Groups
- Understand the difference between Groups and Sets
- Create and use Static Sets
- Create and use Dynamic Sets
- Combine Sets into more Sets
- Use Sets as filters
- Create Sets via Formulas
- Control Sets with Parameters
- Control Reference Lines with Parameters
- Use multiple fields in the color property
- Create highly interactive Dashboards
- Develop an intrinsic understanding of how table calculations work
- Use Quick Table calculations
- Write your own Table calculations
- Combine multiple layers of Table Calculations
- Use Table Calculations as filters
- Use trendlines to interrogate data
- Perform Data Mining in Tableau
- Create powerful storylines for presentation to Executives
- Create powerful storylines for presentation to Executives
- Understand Level Of Details
- Implement Advanced Mapping Techniques

- How to use Groups and Sets to increase your work efficiency 10x
- Everything about Table Calculations and how to use their power in your analysis
- How to perform Analytics and Data Mining in Tableau
- How to create Animations in Tableau
- And much, much more!