• The course provides the entire toolbox you need to become a data scientist
• Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
• Impress interviewers by showing an understanding of the data science field
• Learn how to pre-process data
• Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
• Start coding in Python and learn how to use it for statistical analysis
• Perform linear and logistic regressions in Python
• Carry out cluster and factor analysis
• Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
• Apply your skills to real-life business cases
• Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
• Unfold the power of deep neural networks
• Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
• Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Statistics Essential for Data Science

• Sample or Population Data?
• The fundamentals of Descriptive Statistics
• Distributions
• Estimators and Estimates
• Confidence Intervals
• Hypothesis Testing
• The Fundamentals of Regression Analysis
• Assumptions for Linear Regression Analysis
• Dealing with Categorical Data
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R Programming for Data Science

• R Basics
• Data Structures in R
• R Programming Fundamentals
• Working with Data in R
• Strings and Dates in R

Data Science with R

• Introduction to Business Analytics
• Introduction to R Programming
• Data Structures
• Data Visualization
• Statistics for Data Science
• Regression Analysis
• Classification
• Clustering Data
• Associations

Python for Data Science

• Python Basics
• Python Data Structures
• Python Programming Fundamentals
• Working with Data in Python
• Working with Numpy Arrays

Data Science with Python

• Data Science Overview
• Data Analytics Overview
• Statistical Analysis and Business Applications
• Python Environment and Setup and Essentials
• Mathematical Computing with Numpy
• Scientific Computing with Scipy
• Data Manipulation with Pandas
• Machine Learning with Scikit-Learn
• Data Visualization in Python using Matplotlib
• Web Scrapping with BeutifulSoup
• Python Integration with Hadoop MapReduce and Spark

Machine Learning

• Introduction to Artifical Intelligence and Machine Learning
• Data Wrangling and Manipulation
• Supervised Learning
• Feature Engineering
• Supervised Learning Classification
• Unsupervised Learning
• Time Series Modeling
• Ensemble Learning
• Recommender Systems
• Text Mining

Big Data Hadoop

• Introduction
• Explore Large Datasets
• Hadoop Architecture and HDFS
• Advanced Hadoop MapReduce
• Apache Pig
• Apache Hive
• Apache HBase
• Processing Distributed Data with Apache Spark

Tableau

• Getting Started with Tableau
• Working with Tableau
• Deep Diving with Data and Connections
• Creating Charts
• Adding Calculations to your Workbook
• Mapping Data in Tableau
• Dashboards and Stories
• Visualizations for an Audience