Data Science Training in Gulbarga

MODULE 1

• The Data Science Process
• Apply the CRISP-DM process to business applications
• Wrangle, explore, and analyze a dataset
• Apply machine learning for prediction
• Apply statistics for descriptive and inferential understanding
• Draw conclusions that motivate others to act on your results

MODULE 2

• Communicating with Stakeholders
• Implement best practices in sharing your code and written summaries
• Learn what makes a great data science blog
• Learn how to create your ideas with the data science community

MODULE 3

• Software Engineering Practices
• Write clean, modular, and well-documented code
• Refactor code for efficiency
• Create unit tests to test programs
• Write useful programs in multiple scripts
• Track actions and results of processes with logging
• Conduct and receive code reviews

MODULE 4

• Object Oriented Programming
• Understand when to use object oriented programming
• Build and use classes
• Understand magic methods
• Write programs that include multiple classes, and follow good code structure
• Learn how large, modular Python packages, such as pandas and scikit-learn, use object oriented programming
• Portfolio Exercise: Build your own Python package

MODULE 5

• Web Development
• Learn about the components of a web app
• Build a web application that uses Flask, Plotly, and the Bootstrap framework
• Portfolio Exercise: Build a data dashboard using a dataset of your choice and deploy it to a web application

MODULE 6

• ETL Pipelines
• Understand what ETL pipelines are
• Access and combine data from CSV, JSON, logs, APIs, and databases
• Standardize encodings and columns
• Normalize data and create dummy variables
• Handle outliers, missing values, and duplicated data
• Engineer new features by running calculations • Build a SQLite database to store cleaned data

MODULE 7

• Natural Language Processing
• Prepare text data for analysis with tokenization, lemmatization, and removing stop words
• Use scikit-learn to transform and vectorize text data
• Build features with bag of words and tf-idf
• Extract features with tools such as named entity recognition and part of speech tagging
• Build an NLP model to perform sentiment analysis

MODULE 8

• Machine Learning Pipelines
• Understand the advantages of using machine learning pipelines to streamline the data preparation and modeling process
• Chain data transformations and an estimator with scikit learns Pipeline
• Use feature unions to perform steps in parallel and create more complex workflows
• Grid search over pipeline to optimize parameters for entire workflow
• Complete a case study to build a full machine learning pipeline that prepares data and creates a model for a dataset

MODULE 9

• Experiment Design
• Understand how to set up an experiment, and the ideas associated with experiments vs. observational studies
• Defining control and test conditions
• Choosing control and testing groups

MODULE 10

• Statistical Concerns of Experimentation
• Applications of statistics in the real world
• Establishing key metrics
• SMART experiments: Specific, Measurable, Actionable, Realistic, Timely

MODULE 11

• A/B Testing
• How it works and its limitations
• Sources of Bias: Novelty and Recency Effects
• Multiple Comparison Techniques (FDR, Bonferroni, Tukey)
• Portfolio Exercise: Using a technical screener from Starbucks to analyze the results of an experiment and write up your findings

MODULE 12

• Introduction to Recommendation Engines
• Distinguish between common techniques for creating recommendation engines including knowledge based, content based, and collaborative filtering based methods.
• Implement each of these techniques in python.
• List business goals associated with recommendation engines, and be able to recognize which of these goals are most easily met with existing recommendation techniques.

MODULE 13

• Matrix Factorization for Recommendations
• Understand the pitfalls of traditional methods and pitfalls of measuring the influence of recommendation engines under traditional regression and classification techniques.
• Create recommendation engines using matrix factorization and FunkSVD
• Interpret the results of matrix factorization to better understand latent features of customer data
• Determine common pitfalls of recommendation engines like the cold start problem and difficulties associated with usual tactics for assessing the effectiveness of recommendation engines using usual techniques, and potential solutions.