Python Training and Certification for Data Science and Analytics

• Weekdays – 15 to 16 weeks
• Weekend – 17 to 18 weeks
• FastTrack – 10 to 12 weeks

• Weekdays – 15 to 16 weeks
• Weekend – 17 to 18 weeks
• FastTrack – 10 to 12 weeks

• What is Data Science and what does a data scientist do.
• Various examples of Data Science in the industries and how Python is deployed for
• Data Science applications
• Various steps in Data Science process like data wrangling, data exploration
• selecting the model

• What is exploratory data analysis and building of hypothesis, plotting and other techniques.
• Introduction to Python
• Introduction to Python programming language
• Important Python features, how is Python different from other programming languages
• Python installation
• Anaconda Python distribution for Windows, Linux and Mac
• How to run a sample Python script
• Python IDE working mechanism
• Running some Python basic commands, Python variables, data types and keywords.

• Introduction to a basic construct in Python
• Understanding indentation like tabs and spaces
• Code comments like Pound # character, names and variables
• Python built-in data types like containers (list, set, tuple and dict), numeric (float, complex, int), text sequence (string), constants (true, false, ellipsis) and others (classes, instances, modules, exceptions and more)
• Basic operators in Python like logical, bitwise, assignment, comparison and more, slicing and the slice operator
• Loop and control statements like break, if, for, continue, else, range() and more.

• Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction
• What are access modifiers, instances, class members, classes and objects
• Function parameter and return type functions
• Lambda expressions, connecting with database to pull the data.

• Introduction to mathematical computing in Python
• What are arrays and matrices, array indexing, array math, ND-array object
• Data types, standard deviation
• Conditional probability in NumPy, correlation, covariance SciPy for Scientific Computing
• Introduction to SciPy
• Building on top of NumPy
• What are the characteristics of SciPy
• Various sub packages for SciPy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more
• Bayes Theorem with SciPy

• Introduction to Machine Learning with Python
• Various tools in Python used for Machine Learning like NumPy, ScikitLearn, Pandas, Matplotlib and more
• Use cases of Machine Learning
• Process flow of Machine Learning and Various categories of Machine Learning
• Understanding Linear Regression and Logistic Regression
• What is gradient descent in Machine Learning
• Introduction to Python DataFrames, importing data from JSON, CSV, Excel, SQL database, NumPy array to DataFrame
• Various data operations like selecting, filtering, sorting, viewing, joining and combining, how to handle
• missing values, time series analysis

• What is a data object and its basic functionalities?
• Using Pandas library for data manipulation
• NumPy dependency of Pandas library, loading and handling data with Pandas
• How to merge data objects, concatenation and various types of joins on data objects
• Exploring and analyzing datasets
Data Visualization with Matplotlib
• Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more
• Matplotlib API, Subplots and Pandas built-in data visualization

• What is supervised learning, classification
• Decision Tree, algorithm for Decision Tree induction
• Confusion Matrix
• Random Forest
• Naïve Bayes, working of Naïve Bayes, how to implement Naïve Bayes Classifier
• Support Vector Machine, working process of Support Vector Mechanism
• What is Hyper Parameter Optimization
• Comparing Random Search with Grid Search
• How to implement Support Vector Machine for classification?

• Introduction to unsupervised learning, use cases of unsupervised learning
• What is K-means clustering, understanding the K-means clustering algorithm
• Optimal clustering
• Hierarchical clustering and K-means clustering and how does hierarchical clustering work
• What is natural language processing, working with NLP on text data
• Setting up the environment using Jupyter Notebook
• Analyzing sentence, the Scikit-Learn Machine Learning algorithms Bags of words model
• Extracting feature from text
• Searching a grid, model training, multiple parameters and building of a pipeline

• Introduction to Text Mining
• Introduction to Sentiment
• Setting up API Bridge, between Python and Twitter Account
• Extracting Tweet from Twitter Account
• Scoring the tweet

• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied
• Implement respective ETS model for forecasting

• Introduction to web scraping in Python, various web scraping libraries
• BeautifulSoup, ScrapyPython packages
• Installing of BeautifulSoup
• Installing Python parser lxml
• Creating soup object with input HTML
• Searching of tree, full or partial parsing, output print and searching the tree

• What is the need for integrating Python with Hadoopand Spark
• The basics of the Hadoopecosystem, Hadoop Common
• The architecture of MapReduce and HDFS and deploying Python coding for MapReduce jobs on Hadoop framework
• Understanding Apache Spark
• Setting up Cloudera QuickStart VM
• Spark tools
• RDD in Spark
• PySpark, integrating PySpark with Jupyter Notebook
• Introduction to Artificial Intelligence and Deep Learning
• Deploying Spark code with Python
• The Machine Learning library of Spark Mllib
• Deploying Spark MLlib for classification, clustering and regression parameters and building of a Pipeline

Industry: - Entertainment

Problem Statement: - An online video streaming company wants to know about viewing pattern of its subscriber and build a recommendation system for better viewing experience.

Description: - This real time project will give you a hands-on experience with a movie recommendation system based on which type of movie liked by a user and other viewing choices. You can build a data-driven recommendations. It involves information filtering, rating predictions, learning user preferences and so on. You will work with movie related data contains user details, movie details and others.

Industry: - E-commerce, Research, Lead Generation

Problem Statement: - You need to scrap large amount of meaning full information from the data available over the web.

Description: - You will lead to understand web scrapping using python which involves installation of beautiful soup, libraries, working on targeted data and different web page formats. Important aspects that a learner will understand different possible kinds of objects, string navigations, searching tree deployment, various navigation options such as searching by CSS class, list, functions and keyword arguments.

Industry: - Bank and Finance

Problem Statement: - A major financial Intuition decided to use its data source to detect any possible fraudulent activity and take remedial measures to avoid it.

Description: - With this real time project you will work with some banking transactional data, finding out outliers in the data and classify it based on various parameters. Different statistical techniques will be applied to look for any possible fraudulent activity in the transactional system.

Industry: - e-commerce

Problem Statement: - An e-commerce company wants to know how to deploy targeted selling to its customers

Description: - This is a data science and analysis project to understand once buying habit and sell the product that suits them. The process starts with buying and selling data pre-processing which includes aggregation, cleaning, transforming and collecting consumer buying history to deploy statistical analysis, predictive modelling, and profile creations of consumers to implement more targeted selling.