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Data Science Training Using Python

Data driven decision making is increasing in popularity that is adding value to all business that predicts the future, revolutionise their business, or even disrupt an industry. Data science is basically understanding the patterns means understanding the world. Everything, from a mechanic fixing a car, to a farmer planting crops, to a scientist making some research breakthrough – the identification of a pattern is the first step towards progress. Data science is on the rise, both from a company’s perspective and from an employee’s perspective. This makes data science a great field to get into at the moment.

Course Curriculum:

Data science the most happening & promising career of 21st century. Data Science course is targeted for those candidates who are aspiring to gain full knowledge on all aspects of Data Science including Programing, Statistics, Machine learning as well as business side of Data science, gaining full spectrum of data science skills to deliver end to end Data Science solutions. Python programming has become one of the most preferred languages for the Data Science discipline as on Today. As a part of data science curriculum a quick brush up on python programming will be covered. Data science course comes with a blend of theoretical erudition with practical approach, and several real life use cases in our training conducive to the long term retention of learning and development.


  • Introduction to Data Science

  • Basics of Statistics

  • Advanced Statistics

  • Python Programming for Data Science (Lab)

  • Applied Statistics in Python (Lab)

  • Graphics and Data Visualization, Exploratory Data Analysis in Python (Lab)

  • Machine Learning Concepts

  • Model Validation/Cross validation techniques, Parameter tuning

  • Real World Data Science & Machine Learning Case Studies in Python (Lab)


Data Science programming using python.
Advanced Statistics Concepts – Sampling, Statistical Inference and Testing of Hypothesis
Covers Machine Learning tool kit for Data Science in predicting classification or regression.
Explore and analyze data from the Web, Word Documents, Email, Twitter feeds, NoSQL stores, Relational Databases and more, for patterns and trends relevant to your business.
Build Decision Tree, Logistic Regression and Naïve Bayes classifiers to make predictions about your customers’ future behaviors as well as other business critical events.
Use K-Means and Hierarchical Clustering algorithms to more effectively segment your customer market or to discover outliers in your data
Discover hidden customer behaviors from Association Rules and Build Recommendation Engines based on behavioral patterns
Use biologically-inspired Neural Networks to learn from observational data as humans do
Investigate relationships and flows between people, computers and other connected entities using Social Network Analysis
Case Study for Time Series, K Nearest Neighbor’s Algorithm for Classification, Naïve Bayes Algorithm for Multi Class Prediction, Artificial Neural network with case study, Support vector machines with case study and Recommender Systems. Every topic will be covered in mostly practical way with examples.


Business Intelligence Analyst. ...
Data Mining Engineer. ...
Data Architect. ...
Data Scientist. ...
Senior Data Scientist.

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