DATA SCIENCE Online Training

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The Data Science Online Training  course from US GlobalSoft that provides you with detailed learning in Data Science, Data Analytics, project life cycle, data acquisition, analysis, statistical methods and Machine Learning. You will gain expertise to deploy Recommenders using R programming, and you will also learn data analysis, data transformation, experimentation and evaluation.


The Data Science Online Training  course from US GlobalSoft that provides you with detailed learning in Data Science, Data Analytics, project life cycle, data acquisition, analysis, statistical methods and Machine Learning. You will gain expertise to deploy Recommenders using R programming, and you will also learn data analysis, data transformation, experimentation and evaluation.


There are no particular prerequisites for this training course. If you love mathematics, it is helpful to learn Data Science. You will also get MS Excel self-paced course free with this course.

How will I perform the practical sessions in Online training?

For online training, US GlobalSoft provides a virtual environment that helps in accessing each other’s system. The complete course material in pdf format, reference materials, course code is provided to trainees. US GlobalSoft conductes online sessions through any of the available requirements like Skype, WebEx, GOTOMeeting, Webinar, etc.

DATA SCIENCE Course Syllabus

Module 1 :  Python Programming Language
Part A :  Python Basic  Concepts

  1. Introduction to Python and its involvement with Data Science
  2. Understanding Object Orientation Programming
  3. Installation: Python 3.6 or later version, pip, iPython, Sublime Text Editor, Anaconda(Jupyter and Spyder)
  4. Python Identifiers, Naming Conventions, Variables and Types
  5. Defining Functions, Classes and Methods
  6. Understanding Indentation
  7. Executing sample programs in all Editors
  8. Difference Between Functions and Methods
  9. How to use Python Functions and Methods
  10. Decision making through conditions and Loops
  11. Declaring instances and Workout its accessibility
  12. Understanding global and local variables in python
  13. Instantiating Classes and flow of execution
  14. Accessing Methods, Variables, Global variables and Functions
  15. Working with self and super keywords
  16. Object String representation through __str__ and __repr__
  17. Constructors; Initialization; object: a base class
  18. Inheritance Concept; Overriding and Overloading concept
  19. Constructors with respect to inheritance
  20. Understanding __name__ == ‘__main__’
  21. Exceptions:
  22. Overview of exception
  23. Raising common causing exceptions
  24. Exception Hierarchy
  25. Raising exception at calling method
  26. Handling exceptions through try, except, else and finally
  27. Exception propagation
  28. Customized Exceptions

Part B: Data Structures:

    1. List: Creating, Accessing, Slicing, Manipulating lists, Built-in Functions & Methods in list, Iterating & Enumerating list data and Working with Nested lists.
    2. Tuple, Set and Dictionaries (same above all operations)
    3. Handling conversions of sample data with Data Structures

Part C: Regular Expressions in Python

    1. Patterns, searching, Modifiers, flags
    2. Working with examples to find specific strings, phone numbers, email addresses and filtering html data with regular expressions
    3. File I/O
    4. Working with text files and .csv
    5. Reading and Writing data to the files
    6. Importing required packages to work with .csv

Module2 : Statistics - Probabilities  and Linear Algebra

  1. Statistical thinking in Python and approach of Data Analysis
  2. Fundamental statistics terms and its definitions
  3. Applying basic statistics in Python with NumPy
  4. Cumulative Distribution functions
  5. Modelling Distributions
  6. Graphical exploratory data analysis with Python
  7. Probability theories:
  8. Ranges, Mean, Variance, Standard Deviation and various distributions
  9. Mass and Density functions
  10. Kernel density estimation
  11. Understanding Bayes theorem and predictions*
  12. Estimation
  13. Sampling distributions, bias and Exponential distributions
  14. Hypothesis testing
  15. Hypothesis Test
  16. Testing Correlation and Proportions
  17. Chi-Squared Tests
  18. Errors, Power and Replication
  19. NumPy: N-dimensional array operations
  20. Array creations, conversions, dimensional understandings, shaping, reshaping, generating sample large datasets, Linear algebra functionalities and numerical operations etc…
  21. SciPy: High-level Scientific Computing
    1. Linear Algebra operations
    2. Interpolation
    3. Optimization and fit
    4. Statistics and random numbers
    5. Numerical Integration
    6. Fast Fourier transforms
    7. Signal processing and image manipulation


Module3 : Data Mining & Data Analytics (Data Harvesting, Cleansing, Analyzing and Visualizing)
Part A :Pandas and NumPy Functionalities:

    1. Introduction
    2. Pandas DataFrame basics
    3. Understanding data, looking at columns, rows and cells
    4. Subsetting Columns, Rows with methods
    5. Grouped and Aggregated Calculations

i.          Frequency Means and Counts

  1. Basic plot
  2. Pandas Data Structures
    1. Creating your own data (Series and DataFrame)
  3. Series (also called as Vector) Object operations
    1. Broadcasting and Scalar operations
  4. DataFrame Broadcasting (Vectorized)
  5. Making changes to Series and DataFrame

i.    Adding additional Columns
ii.   Dropping values

  1. Exporting and Importing Data

Part B :  Introduction to Plotting:

  1. Introduction
  2. Matplotlib
  3. Statistical Graphics using matplotlib
  4. Univariate
  5. Bivariate
  6. Multivariate Data
  7. Seaborn Library Plotting methodology
  8. Univariate, Bivariate and Multivariate
  9. Pandas Objects Plotting
  10. Histogram, Density Plot, Scatterplot, Hexbin Plot and Boxplot
  11. Seaborn Themes and Styles

Part C : Data Manipulation:

    1. Data Assembly
    2. Concatenations and Merging Multiple datasets
    3. Missing Data:
    4. Introduction
    5. What is a NaN Value
    6. Working with merged data, user input values and Re-indexing
    7. Working with missing data
    8. Finding and Counting missing data
    9. Cleansing missing data
    10. Calculations with missing data
    11. Conclusion Understanding Multiple Observations (Normalization)

Part D : Data Munging:

    1. Understanding Data Types
    2. Converting types
    3. Categorical Data
    4. Convert to Category
    5. Manipulating Categorical Data
    6. Strings and Text Data
    7. String Subsettings
    8. String Methods
  1. String Formatting
  2. Apply and Groupby Operations:
    1. Introduction
    2. Functions
    3. Apply over a Series and DataFrame
    4. Apply- Column-wise and Row-wise operations
  3. Groupby Operation:
    1. Aggregate Methods and Functions
  4. The datetime Data Type:
    1. Python’s datetime Object
    2. Loading, Converting, Extracting Date components
    3. Date Calculations
    4. Datetime Methods
    5. Subsetting datetime, Date Ranges, Shifting Values, TimeZones

Module 4 : Machine Learning  (Data Modelling)

    1. Linear Models
    2. Linear and Multiple Regressions using statsmodels and sklearn
    3. Generalized Linear Models
    4. Logistic and Poisson Regressions using statsmodels and sklearn
    5. Survival Analysis
  1. Model diagnostics
    1. Residuals
    2. Comparing Multiple Models
    3. k-Fold Cross-Validation
  2. Regularization
  3. Clustering
    1. k-Means, Dimension Reduction with PCA (Principal Component Analysis)
    2. Hierarchical Clusterings
    3. Conclusions

Practical Data Analysis and Understandings
Data Science Interview Questions Discussions (2 sessions)

Note: Keeping main objective as “Understanding” All the above topics are covered with logical and programmatic approach in Python. Also please note that Content order is NOT compulsorily followed at the time of delivering subject and knowledge.


Certification assistance provided with proper guidance and certification notes.