Data Science Online Training|Online Data Science Training in USA, UK, Canada, Australia, India

1. A1Trainings Data Science Online Training|Online Data Science Training in USA, UK, Canada, Australia, India

2. DATA SCIENCE COURSE CONTENT

3. Data Science Introduction and Toolbox :Getting Started with Github • Introduction to Git • Introduction to Github • Creating a Github Repository • Basic Git Commands • Basic Markdown

4. Getting Started with R • Overview of R • R data types and Objects • Getting Data In and Out of R • Subsetting R Objects • Dates and Times

5. Getting Started with R • Control structures • Functions • Scoping rules of R • Coding Standards for R • Dates and times

6. Getting Started with R • Loop Functions • Vectorizing a Function • Debugging • Profiling R Code • Simulation

7. Data Extraction, Preparation and Manipulation ( R, MYSQL, HDFS, HIVE and SQOOP):Data Extraction • Downloading Files • Reading Local Files • Reading Excel Files • Reading JSON • Reading XML • Reading From WEB • Reading From API

8. Data Extraction • Reading From HDFS • Reading From MYSQL • SQOOP • Reading FROM HIVE • Saving and Transporting Object • Reading Complex Structure

9. Data Preparation • Subsetting and Sorting • Summarizing Data • Creating New Variable • Regular Expression • Working With Dates

10. Data Manipulation • Managing DataFrame with dplyr package • Reshaping Data • Merging Data

11. Descriptive Statistics • Univariate Data and Bivariate Data • Categorical and Numerical Data • Frequency Histogram and Bar Charts • Summarizing Statistical Data • Box Plot, Scatter Plot, Bar Plot, Pie Chart

12. Probability • Conditional Probability • Bayes Rule • Probability Distribution • Correlation vs Causation • Average • Variance • Outliers • Statistical Distribution • Binomial Distribution • Central Limit Theorem • Normal Distribution • 68-95-99.7 % Rule • Relationship Between Binomial and Normal Distribution

13. Hypothesis Testing • Hypothesis Testing • Case Studies

14. Inferential Statistics • Testing of Hypothesis • Level of Significance • Comparison Between Sample Mean and Population Mean • z- Test • t- Test

15. ANOVA (f- Test) • ANCOVA • MANOVA • MANCOVA

16. Regression and Correlation • Regression • Correlation • CHI-SQUARE

17. Principal Of Analytic Graph Introduction to ggvis • Exploratory and Explainatory • Design Principle • Load ggvis and start to explore • Plotting System in R • ggvis - graphics grammar

18. Lines and Syntax • Properties for Lines • Properties for Points • Display Model Fits

19. Transformations • ggvis and dplyr

20. HTMLWIDGET • Geo-Spatial Map • Time Series Chart • Network Node

21. Predictive Models and Machine Learning Algorithm - Supervised Regression RegressionAnalysis • Linear Regression • Non- Linear Regression • Polynomial Regression • Curvilinear Regression

22. Multiple Linear Regression • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

23. Logistic Regression • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

24. Time Series Forecast • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

25. Predictive Models and Machine Learning Algorithm - Supervised ClassificationNaïve Bayes • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

26. Support Vector Machine • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

27. Random Forest • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

28. K- Nearest Neighbors • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

29. Classification and Regression Tree (CART) • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

30. Predictive Models and Machine Learning Algorithm - UnsupervisedK Mean Cluster • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

31. Apriori Algorithm • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

32. Case Study : Customer Analytic - Customer Lifetime Value • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

33. Text Mining, Natural Language Processing and Social Network AnalysisNatural Language Processing • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

34. Social Network Analysis • Collect Data • Explore and Prepare the data • Train a model on the data • Evaluate Model Performance • Improve Model Performance

35. Capstone Project • Saving R Script • Scheduling R Script

36. Contact Info • Address: Madhapur, Hyderabad. • Email: contact@a1trainings.com • Call us: 7680813158 • Web: www.a1trainings.com