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Data Science with Python

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About Data Science :

  • Is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data..
  • Is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems".


  • Need for Data Science
  • Tools for Data Analytics
  • Why Python for Data Science
  • Python and Anaconda Installation
  • Python on Command prompt
  • IDEs - Spyder
  • Jupyter Notebook
  • Python as a Calculator
  • Python Basic Structures - Variables, Strings and Numbers
  • Data Structures: Lists, Sets, Dictionaries, Tuples.
  • Comparison operators in Python
  • Conditional Statements – If, Else, Elif
  • Loops – While and For loops
  • List Comprehensions
  • Built in Functions
  • User defined functions
Mathematical computing with Numpy:
  • Numpy Arrays: Creation, Indexing, Slicing, Filtering
  • Random Module in Numpy
  • Loading and saving data with Numpy
  • Statistics with Numpy
  • Confidence Intervals with Numpy
Working on Excel like data with Pandas:
  • Pandas Foundation
  • Pandas advanced indexing and slicing
  • Importing and saving csv/excel data in Python
Visual Analysis with Matplotlib and Seaborn:
  • Exploratory Data Analysis (EDA)
  • Simple & Multiline plots
  • Customising plots
  • Saving graphs
  • Box plot, Violin plot, Swarm plot
  • Count plot, Heatmaps
Advanced Pandas tools for Data Wrangling and Data Analysis:
  • Groupby – tool for advanced analysis
  • Multi Index
  • Concatenating Data frames
  • Merging Data frames
  • Pivot and Pivot table
Data preparation for Machine Learning:
  • Missing values
  • Categorical data handling
  • Data transformation
  • Introduction to Feature Engineering
  • Introduction to Machine Learning
  • Types of Machine Learning
  • Introduction to Supervised Machine Learning - Regression
  • Understanding Linear Regression
  • Understanding Gradient Descent
  • Implementing Linear Regression in Sci-kit Learn
  • Overfitting and Underfitting
  • Introduction to Supervised Machine Learning - Classification
  • Logistic Regression
  • K-Nearest Neighbours
  • Decision Trees
  • Concept of Gini and Entropy
  • Ensemble Models - Random Forest
  • Classification Metrics
  • Cross-Validation
  • Hyper-parameter tuning
  • Concept of Boosting and Bagging

  • Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.
  • The term "Data Science" was coined at the beginning of the 21st Century. It is attributed to William S. Cleveland who, in 2001, wrote "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics."

For Students: The program advances a student's education and helps prepare them for the skills demanded by leading employers.

For Universities: It is important for universities to provide an education that is relevant to industry to ensure gainful employment of graduates. Universities may also find ways to use Data Science to analyze their own data.

For industry professionals: To Switch their domain into one new technology.

Entire training including Hands on Sessions are done by Industry experts with 10+ yrs of experience in their respective field.

Yes, We will be mailing you the certificate on completion of the course, Authorized by Axelta.

There are no specific prerequisites. Knowledge on any Data base would be certainly beneficial.Axelta has built the deck and tutorials/practicals in a simple and easy way so that anyone can learn from it. During the training our experts will help you understand all aspects.

Axelta Systems will connect you to its recruiting partner. They will provide you the placement assistance.

You can enroll directly from our website link shared by our team.

Yes, You will get life time access to the recorded training videos and content. You can use these recoded videos to cover up for your missed session(s) or to revise your training lessons in future.

You will be automatically added to the Axelta alumni Google Group (Axelta IoT-Bootcamps) after completion of the course. It’s a very active group and we at Axelta make sure that we answer all your queries/questions.

Role: Data Science : Senior Associate
Experience: 2 to 4 years
Snippet of Job Description: At least 4-6years experience in machine learning/statistics role. Good experience implementing data mining and machine learning algorithms and analyzing large datasets.
Role: Lead - Data Science / Machine Learning
Experience: 4 to 6 years
Snippet of Job Description: 2-4 years overall experience with minimum 1-3 years in Analytics delivery experience with hands on quantitative research statistics Presentation Passionate about solving business problems, providing business Insights, and statistics....
Role: Senior Decision Analyst - Data Science
Experience: 5 to 10 years
Snippet of Job Description: Workwith multiple , complex data sources at large scale Utilize big data and machine learning to build predictive models including but not limited to credit risk , fraud , and marketing....