DATA SCIENCE Online Training

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US GlobalSoft offers a Data Science Online Training course for our students. This course provides detailed learning in Data analytics, Data acquisition, Machine learning, Project life cycle, and Analysis statistical methods. During training, you will gain expertise for deploying Recommenders with the help of R programming. In this training, you will also learn data transformation, data analysis, experimentation, and evaluation.

Introduction

The Data Science Python Training is one of the best online courses, designed and conceived by expert trainers in Python Programming in US GlobalSoft. The certification of this course may help you become a proficient Python programmer. Our company offers detailed learning in Data Science, Data Acquisition Analysis, Machine Learning, Project Life Cycle, and Analysis Statistical Methods. During training, you will gain the knowledge of data transformation, data analysis, experimentation, and evaluation taught by certified trainers of our company. The course of Data Science with Python gives a complete overview of Python’s Data Analytics tools and techniques. Python is very important for different Data Science roles for students. If you acquire the knowledge of Data Science in this course then may become a Data Scientist. The Data Science with Python is the best online python course that provides the basic concepts of Python programming. During the online course, you will also study Data Analysis. Web Scraping, NLP, Machine Learning, and Data Visualization. After completing the Data Science Course successfully from our company, you will become the master of essential tools of Data Science with Python. The question arises why python in this course. Python is a very important part of this course and a good course to get a job easily in technical companies. Thus, Python for data science is good for this course and you can also learn python programming with this online training. You can also study or learn other IT courses online that are provided by our company. We also offer Online IT certification to students who completed their course.

Data Science

It is the study of data in the form of graphs, mathematical models, and statistics for data analysis in various needs. Thus, programming for data science with python is the most effective and preferred for those students who are interested in this course. The python data science with python will provide you about the fundamentals of python programming that make it efficient to deal with cleaning techniques by using data science python libraries, and data manipulation. It is important to note that Data Science with Python would be a difficult thing to work on in this course.

Benefits of course

Data Science is a growing field with Python in the entire world that helps in getting a job with a high salary in a multinational company. As increasing the demand for Data Science, you will grow an estimated about 1581 percent in the year 2020. If you completed this course successfully then get an excellent job with a high salary.

Features of Data Science in Python

There are some of the features of Data Science in Python are as given below
• Understanding python data science libraries
• Access to real-life scenarios
• Learning basic concepts of data science python visualization
• We will offer course certification guidance
• Our organization help to interview question with answers by our experts

Pre-requisites

In this course, there are no particular prerequisites and if you like mathematics then it is very helpful in learning Data Science. During training, you will also get MS Excel self-paced course free with this course.

Use of this course

The Data Science with Python will be very beneficial for professionals with a skill for programming. It will also suitable for new users and individuals who are interested in pursuing a career in the programming field. This course will use in learning and understanding python programming from scratch.

How will I perform practical sessions in training

US GlobalSoft offers a virtual environment, which helps to access each other’s systems in this online training. We provide the material of the entire course, which is available in reference materials, pdf format, and course code to our students. Our company conducts online sessions through any of the available requirements such as a webinar, GOTO Meeting, Skype, WebEx, and many more.

Main Features of course

There are main features of this course that are offered by our organization are as given below

• Online Courses 40-45 hours – Every session will be about an hour according to the schedule for an instructor-led live online training program. Our company offers weekend or weekdays schedules. If you miss any class then have the flexibility for watching the recording of the class. You will be provided recorded videos to cover all the topics in the whole training course for a self-paced video training program. The duration of the entire online training course is about 40 to 45 hours.
• Access Course Materials Lifetime – During training, you will get six months of access to the Learning Management System (LMS). The recordings of the class, sample codes, documents, all installation guides, and class presentations are available in the LMS. We also provide access to study or course materials for a lifetime.
• Assignments or Quizzes: 10 hours – We provide assistance or installation guides to set up an auto evaluation.
• Project/Case Study: 5 hours – You will work on a real-time project based on the case study on any of the selected use cases. We also offer problem statements and data sets for students.
• 24x7 Support – Our company offers a 24x7 online support team and always available for students to solve technical queries during the course. The queries of students are tracked as tickets and will get a guaranteed response. The support team can also provide live support by accessing the machine remotely if required.
• Get certified – We offer certification assistance with proper guidance and certification dumps to students after completing the course that helps in getting a job in a multinational company. If you complete this course then we offer Data Science certification to our students.
• Resume and Placement Assistance – Our company works with several consulting companies in the United States. The expert of our organization provided a dedicated resume with interview assistance after completing the entire course for students. US GlobalSoft also provide Online Training and Job Placement for those students who got a certification of this course

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

Certification assistance provided with proper guidance and certification notes.