Data science Course in Pune

INTRODUCTION

TRAINING FOR A NEW AGE CAREERS & HELPING YOU CREATE A BETTER TOMORROW

What is Data Science?

Data is the new oil for all industries. Data science is the study of data. It involves developing methods of acquiring and analyzing data to effectively, extract useful information, and help businesses to solve their priority problems/take decisions.

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science is related to data mining and big data. Data mining is a subset of data science.

Data science is more closely related to the mathematics field of Statistics, technologies (Python, R, Machine learning, Deep Learning, AI, Tableau/Power BI, Big data), any domain expertise which includes the collection, organization, analysis, get insights, build and deploy model and presentation of data.

Why Data Science?

Now a day’s everyone is discussing data science. We are in the era of cutting-edge information technology where the computing power of computers has increased exponentially, storage capacity has increased tremendously and the cost of both has come down significantly. So data and computing power have become weapons and with the help of data science, we can solve business problems at hand using already built-in algorithms.  Be it, to predict sales in the upcoming month or next 3 months, or predict if the person is having cancer or doing market basket analysis, data science can help to solve all such problems. It helps businesses to increase profit, revenues, sales, increase productivity, alerts business on inventory, alerts if patients will have trouble in the future, and so on so forth. Across all domains, data science is being used. Currently, the demand for a supply of data scientists is inversely proportional.

All companies are seeking good data scientists, data analysts, data engineers, ML engineers, Data Visualizer, to help them to find out insights from the data and solve their and client’s business problems.

Anyone from any industry can transition into the data science field including freshers without requiring much programming knowledge. You need to have passion, patience, and perseverance to become a data scientist or one of the roles – Data Analyst, Data Engineer, ML Engineer, Data Visualizer.

Hope this helps. Let me know if you need any more details from my end.

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  • To become a professional in different roles(Data Analyst, Data Engineer, Jr. ML Engineer, ML Engineer, Sr. ML Engineer, Data Scientist, Data Leader) in Data Science world
  • To provide insight into real world scenarios of life as a Data science professional in IT Industry
  • To provide students with indispensable knowledge and skills which will enable them to pursue career in the IT Industry as a data science professional in capacity of different roles
  • To provide skill based professional training for career advancement
  • To provide the entry/mid level professionals with concrete building blocks to create a path for full employment and successful career
  • To provide guidance/assistance for ‘How to crack interview’, Do’s and Don’ts of an Interview
  • And last but not least To find a Job in Data Science domain
  • The course specifically designed as per the requirement from various IT Clients
  • Certified Trainers from the IT industry with a rigorous understanding of the specialized domain
  • Lecture contents are prepared by Industry expert
  • Live Projects based training under the guidance of industry experts
  • Weekly workshop from industry experts
  • An innovative teaching methodology which delivers in-depth knowledge with quality tailored programs that maximize a return on your investment.
  • Strategic Association for global certification

The course will have regular classroom Lectures, Practical Sessions, Seminars, Tutorials, Case Studies, Assignments and Exams.

230 Hours –  4 Months (monday to friday) – (2 hours a day)
  • Undergraduates, Graduates and Post-Graduates
  • Need analytical and logical thinking
  • No Previous Programming Experience necessary
  • Job aspirants / fresher looking to commence career in Data Science domain
  • Passionate about Data Science or programming
  • Can assist Python developer
  • Data Analyst
  • Makes foundation for Data Engineer, ML Engineer
  • This course will make entry to data science domain/world
  • Sr. ML Engineer, Data Scientist
  • Tableau developer, Tableau Consultant
  • Data Engineer, Data Visualizer
  • Associate ML Engineer
Program Contents / Syllabus

Big Data Programming

  • Java programming for MapReduce
  • SQL fundamentals
  • Linux fundamentals

MongoDB

  • Introduction to NoSQL Databases
  • Introduction to NoSQL and MongoDB
  • MongoDB installation
  • Importance of NoSQL
  • CRUD operations
  • Data modeling and schema design
  • Data management and administration
  • Data indexing and aggregation
  • MongoDB security
  • Working with unstructured data

statistics for Analytics

  • Introduction to statistics
  • Logistic regression
  • Decision trees and random forest
  • Different Regression technique

Data Processing Tool

  • Data Analytics in Excel
  • Concepts of finance
  • Concepts of economics
  • Hands-on: Inferential statistics, descriptive statistics, simple and multivariate regression, and confidence intervals
  • Data Analytics Using SQL
  • Introduction to MySQL
  • Working with MySQL and MySQL IDE: Installation and setup
  • Introduction to SQL queries: DDL queries (create and select) and DML queries (alter, insert, etc.)
  • Working with joins, group, and filter
  • Writing complex SQL queries for data retrieval and the import and export of data and database tables
  • Data Warehousing

Data Analytics Using Python

  • Introduction to Python
  • Python basic constructs
  • OOPs in Python
  • NumPy for mathematical computing
  • SciPy for scientific computing
  • Data manipulation
  • Data visualization with Matplotlib
  • Implementing statistical algorithms using Python

Hadoop and Its Ecosystems

  • Hadoop installation and setup
  • Introduction to Big Data and Hadoop
  • Understanding HDFS and MapReduce
  • Deep dive into MapReduce
  • Introduction to Hive
  • Advanced Hive and Impala
  • Introduction to Pig
  • Flume and Sqoop

Apache Spark and Scala

  • Scala programming
  • Spark framework
  • RDD in Spark
  • DataFrames and Spark SQL
  • Machine Learning using Spark (MLlib)

Business Intelligence and Data Mining

  • What is data warehousing? What is data mining? Use cases and applications
  • Creating data models for large data warehouses
  • Different types of data models: Star, snowflake, and hybrid; which is the right model?
  • Integration of Hadoop and Spark with an ETL tool
  • Building workflows using Informatica for the integration with HDFS, Hive, MapReduce, etc.
  • Performance Tuning of ETL systems for processing large datasets

Python for Spark

  • Introduction to PySpark
  • Who uses PySpark?
  • Why Python for Spark?
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Numbers
  • Python files I/O Functions
  • Strings and associated operations
  • Sets and associated operations
  • Lists and associated operations
  • Tuples and associated operations
  • Dictionaries and associated operations

Hands-On:

  • Demonstrating Loops and Conditional Statements
  • Tuple – related operations, properties, list, etc.
  • List – operations, related properties
  • Set – properties, associated operations
  • Dictionary – operations, related properties

Python for Spark: Functional and Object-Oriented Model

  • Functions
  • Lambda Functions
  • Global Variables, its Scope, and Returning Values
  • Standard Libraries
  • Object-Oriented Concepts
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways

Hands-On:

  • Lambda – Features, Options, Syntax, Compared with the Functions
  • Functions – Syntax, Return Values, Arguments, and Keyword Arguments
  • Errors and Exceptions – Issue Types, Remediation
  • Packages and Modules – Import Options, Modules, sys Path

MACHINE LEARNING – FOUNDATION

  • Introduction to Machine Learning (ML)
  • Data Science Project Life Cycle
  • Linear Regression (Regression)
  • Logistic Regression (Classification)
  • Decision trees & Random Forests (Regression/Classification)
  • Model evaluation techniques for Regression and
  • classification
  • Gradient Descent & Optimization
  • Miscellaneous (Bias, Variance, Overfit model, Underfit
  • Model, Noise, Balance/Imbalance dataset, Scaling)
  • Machine Learning Cheatsheet
  • Interview questions
  • Flashcards
  • Capstone Project

MACHINE LEARNING – INTERMEDIATE

  • Dimensionality Reduction using PCA
  • KNN (K- Nearest Neighbors)
  • Naïve Bayes classifier
  • K-means clustering technique
  • Support vector machines (SVM)
  • Time series forecasting
  • Machine Learning Cheatsheet
  • Interview questions
  • Flashcards
  • Capstone Project

MACHINE LEARNING – ADVANCED

  • Apriori Algorithm
  • Recommender System
  • LDA
  • Anomaly Detection
  • Ensemble Learning
  • Stacking
  • Optimization
  • Introduction to Neural Network
  • How to deploy the model
  • Interview Questions
  • Flashcards
  • Capstone Project

Tuning Models

  • Why model tuning?
  • What is model tuning?
  • What are parameters
  • What are Hyper-parameters
  • What is Hyper-parameter tuning?
  • Types of Hyperparameter tuning:
  • Grid Search
  • Random Search

Hands-On:

  • Performing Grid Search Hyperparameter Tuning to Increase model accuracy
  • Performing Random Search Hyperparameter Tuning to Increase model accuracy

Data Visualization Using Tableau

  • Introduction to data visualization and the power of Tableau
  • Architecture of Tableau
  • Working with metadata and data blending
  • Creation of sets
  • Working with filters
  • Organizing data and visual analytics
  • Working with mapping
  • Working with calculations and expressions
  • Working with parameters
  • Charts and graphs
  • Dashboards and stories
  • Tableau Prep
  • Integration of Tableau with Big Data tools like Hadoop and Spark

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