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data science with python
About Data Science With Python

Data Science with Python: Unleash the Power of Data Insights Python has emerged as a leading programming language for data science, empowering organizations to extract valuable insights from vast amounts of data. With its rich ecosystem of libraries and frameworks, Python provides a powerful and flexible environment for data analysis, machine learning, and predictive modeling. With Python's ease of use, extensive community support, and vast array of libraries, it has become a go-to language for data scientists. Embrace Python for data science and unlock the potential of your data, gaining valuable insights that can transform your business.


About Course & Curriculam

About The Course

Data Science with Python online course is meant by our leading specialists in Python programming. If you completed this online course then you may be a good and commanding Python technologist. Python for Data science contains informative data or information that is needed for you to become a Successful Developer of Data science python.

Our instructors will teach the essential Benefits of e-learning throughout the Online Training course. We provide Training videos of the lost category to students that cover the course if you miss any categories. Our organization additionally offers python for data science certification. If you completed the course that helps in obtaining employment during a smart company with a high regular payment. We have a tendency to additionally give IT Training and job assistance to you when finishing the course. 

Data Science course is that the study of knowledge within the type of mathematical models, statistics graphs for knowledge analysis in varied desires thus programming for Data science with python is that the only and most well-liked. We provide python data science interview questions and tutorials to students during which all the fundamentals ideas of python programming. This tutorial helps students to understand the basics of data science with python course. when finishing this online course, you may be professional in addressing Data manipulation and cleansing techniques by victimizing Data science libraries. it’s necessary to notice that Data science while not python is extremely troublesome. 

There are some of the features of Data Science in Python are given below – 

  • Insight into python data science libraries
  • We assist you in determination interview queries with answers
  • Access to real-life scenarios
  • Learn or study basic ideas concerning what’s data science
  • We will Provide data science certification after completing the course.
  • Directed you with data science interview queries

This Python Data Science course is useful to professionals with an ability for programming. you’ll be able to additionally learn Data science throughout the training course. This online course will be helpful for brand spanking new users and professionals that wish to pursue a career within the programming field. throughout the training course, you’ll be able to learn and perceive Python programming with python Data science. 

Data Science could be a growing field with Python within the entire world that helps in obtaining employment with a high regular payment during an international company. As the demand for Data Science, you may grow associate calculables concerning 1581 % now a days. If you completed this course with success then get a wonderful job with a high regular payment. 

  • 24×7 support to student – If any student faces any issues within the conception of the course with the sensible so, you’ll be able to sit down with your trainer to resolve their downside. Our organization provides 24×7 support to the students for determination their problems with our instructors. There are another choices to contact your teacher like email and decision by phone. Some students don’t seem to be smart in communication in any live session with their teacher. So, you’ll be able to additionally discuss problems along with your trainers through mail to resolve them. you’ll be able to additionally contact by employing a smartphone if you have any issue with this online course. We have a tendency to additionally supply online tutorials, thus you’ll be able to access them anytime to appear up the solutions for his or her issues. 
  • Expert Trainers – We’ve professional instructors that have glorious knowledge with in the subject that they teach to students throughout online Training. The capabilities of our trainers are to resolving the issues that are asked or faced by students. 
  • Job Assistance – We have a tendency to additionally provide Data science jobs when completion of the online Training course. The responsibility of our organization is that each one of the students that the who completed their course are placed in a well established company with a decent package. we have a tendency to additionally facilitate students in counseling, ability assessment, creating skilled resumes, and lots of to get a good job. 

Course Curriculam

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
  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

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
  1. 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
  1. 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
  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

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.


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