What is Data Science?

Data Science is an emerging field that focuses on automated methods and techniques to analyze (, Large amount of data). The ultimate goal is to create new knowledge based on actual evidence. The Data Science draws conclusion from objective data while many traditional data analysis techniques.

Example:

  •  Data Science is like analyzing a photo instead of a picture drawn from the mind.
  •  The applied methods and techniques are derived from many fields, mathematics (mainly statistics), Machine learning, and software engineering.
  • Domain experts need to participate in data science projects to make sense to results without making interpretation errors.

Learn this powerful object oriented programming language with its dedicated library for data analysis and predictive modeling for data munging, data engineering, data wrangling, website scraping, web app building, etc.

Data Science with Python

At innomatics Academy, the training curriculum will cover all the stages of Python Data Science from Data extraction, munging, cleansing, modelling and visualization. This course is comprehensively designed to discuss the core concepts related to Python by laying specific focus on the Data Science. The training curriculum allows the participants to have a clear understanding of the data science, statistical concepts relevant to data science, programming using the python, machine learning concepts and in-depth knowledge on the implementation of libraries and models to solve real-world data science challenges.

Key concepts of Python programming using the Python libraries

This course is designed to meet all the industry standards and the best practices that give the participants a hands-on practice on the different applications,

Mathematical Libraries:

  • NumPy
  • Pandas
  • SciPy
  • Visualization Matplotlib, Seaborn

Machine Learning:

  • Scikit-learn

Natural Language Processing

  • NLTK
  • Spacy

Deep Learning

  • Tensorflow
  • Keras

 

Course Details

  1. To understand the vital nature of data for organizations.
  2. To learn the conceptual framework of machine learning.
  3. To explore and Analyze data using supervised and unsupervised learning techniques in Machine Learning.
  4. To develop knowledge learning models using Python.
  5. Learn and build multiple systems using Natural Language Processing (NLP) branch of Machine Learning of Data science where it deals with text.
  6. Know when to learn and Develop Deep Learning Framework for various Use-cases.

Learning Outcomes:
Upon completion of the course, a participant will be able to:

  1. Understand the importance of data, information and knowledge.
  2. Understand various use case and business scenarios of the real world.
  3. Get hands on experience in exploring, analyzing and modeling real world business scenarios.
  4. Understand machine learning framework and develop machine learning models.
  5. Undersand various NLP system
  6. Understand the framework of Neural network and Deep Neural Network using TensorFlow and Keras in Python.

Target People:

  • Who are willing to kick start their career in Data Science
  • Developer who have basic programming language Knowledge.
  • Students who want their career to get started as a Data Scientist.
  • People who wanted them to solve real-time problem to a solution-oriented results through data analysis.
  • People who wanted to be problem solver to an organization
  • To scale up them to be an integral part of an organization.

System Configuration:

  1. Minimum expected configuration
    a. Expected configuration
    i. RAM – 4 / 8GB atleast (Higher recommended)
    ii. Storage – 1-2 TB atleast
    iii. Processor – Intel Dual Core, i3 family atleast, expected i5 to i7
    iv. Windows, Linux and Mac

Module – 1: Basics of Python Data Structures

  • Introduction to Python basic syntax
  • Basics of Data types

Module – 2: Python Statements, Methods, Functions and Expressions

  • Python Statements
  • Methods and Functions in Python
  • Expression

Module – 3: Advanced Python

  • Modules and Packages
  • Python Datetime
  • File Handling
  • Errors and Exceptions Handing

Module – 4: More Advanced Python

  • Object Oriented Programming
  • Regular Expression

Chapter 2: Python for Data Analysis

  • Introduction of NumPy
  • Introduction to Pandas
  • Data Visualization

Chapter 3: R Programming

  • Basics of R
  • R for Data Analysis

Chapter 4: Statistics and Probability

  • Descriptive Statistics
  • Inferential Statistics

Chapter 5: Exploratory Data Analysis

  • Data processing using MS Excel
  • Data Preprocessing using Python with Use case.
  • EDA with Visualization

Chapter 6: Tableau for Story Telling

  • Tableau Basics
  • Time Series, Aggregating, and Filters
  • Dashboard
  • Joining and Blending Data, PLUS: Dual Axis Charts
  • Calculations, Advanced Dashboards, Storytelling
  • Advanced Data Preparation
  • Linear Regression
  • Logistic Regression
  • Naïve Bayes classifier
  • Feature Reduction / Dimensionality reduction
  • Regularization methods

Chapter 7.1: Machine Learning - Beginning

  • Linear Regression
  • Logistic Regression
  • Naïve Bayes classifier
  • Feature Reduction / Dimensionality reduction
  • Regularization methods

Chapter 7.2: Machine learning - Advance

  • Rule based in Supervised Learning
  • Distance Based Approach (k-NN)
  • Mathematical Approach – Support Vector Machine (SVM)
  • Ensemble Models
  • Model Selection
  • Unsupervised Learning
  • Clustering
  • Feature Reduction / Dimensionality reduction

Chapter 8: Natural Language Processing (NLP)

  • Introduction to Text Mining and its Application
  • Structured and Unstructured Data
  • Extracting Unstructured text from files and Websites
  • Processing with Raw Text
  • Categorizing and Tagging
  • Introduction to the Fundaments of information retrieval
  • Matrix factorization: Singular Value Decomposition (SVD)
  • Text Indexing
  • Text classification
  • Sentiment analysis

Chapter 9: Artificial Intelligence

  • Introduction to Neural Networks
  • TensorFlow
  • Building Neural Network with TensorFlow
  • Convolutional Neural Networks (CNN)
  • Building Convolution Neural Network in Python
  • Keras (Backend TensorFlow)
  • Recurrent Neural Networks (RNN)
  • Building Recurrent Neural Networks with TensorFlow and Keras
  • Autoencoders

Chapter 10: Deployment

  • Deploying Machine Learning and Deep Learning models into AWS
  • Pickle Files
Instructor Led training 24 Hrs
Instructor Interaction Yes
Live Support Post Training 6 months
Simulated Projects 2
Capstone/Hands On/Real Time Project 2
Innomatics Tech Hub – Data Science with Python Certificate Yes

Why Data Science and Why Python?

According to Forbes, Data Science is one of the sexiest jobs of 21st century. The number of problems that can be solved using the data science are endless. As a matter of fact, there is a massive shortage of data scientists in the current market.
On the other hand, Python is considered as one of the most popular tools used by the Data Analysts and Data Scientists. It’s extremely capable programming language with a simple construct makes it one of the most vital tools in the repository of the Data Scientists.

What are the prerequisites of this course?

To make the most of this course, the participants are required to have Basic familiarity with the computing and programming concepts and Good knowledge of mathematical and statistical concepts

Who is the right candidate for this course?

This course is extensively useful for the aspirants who are looking for a career progression in the field of Data Science and also who have the designations including (but not limited) to – Technical Analyst, Data Analyst, Database Developer, Hadoop Developer, Big Data Architect, Programmer Analyst, Big Data Engineer, Business Analyst (Technical) etc.

What are the training materials provided?

For all the training modules that are covered in this course, adequate materials and good references will be provided to the participants. In the case of online interactive trainings, every session will be recorded and uploaded in the LMS, giving the participants a feasibility to recap their completed training sessions.

Is Certification offered and if so, how do you earn?

After the completion of course, the participants will undergo a certification examination. Based on their performance in the assignments, projects and the final examination, certificates will be issued to the participants.