Data-Science-certification-Training

What is Data science?
Data science is a domain and it's a combination of expertise in programming, knowledge of mathematics and statistics to extract meaningful insights from given data of any Industry. And practitioners may apply ML algorithms to numbers, text, images, video, audio, and more that can perform tasks which generate insights that analysts and business users translate into tangible business value.

Why to learn data Science?
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. There is a growing need among companies for professionals to know ins and outs of Data Scientists. A successful data professionals can understand or to uncover useful intelligence for organizations. They  possess a level of flexibility and understanding to maximize returns at each phase of the process.

What Does a Data Scientist Do?
Data Scientist need to possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms. Leadership quality needed to deliver tangible results to various stakeholders across an organization or business. 

It's Really Good to Be a Data Scientist Right Now!
Data is the new corporate currency, as advancing impact on the data science sector is far-reaching and, as a result, a range of new roles and skill-set are in demand. The skills required as a data analyst, IT architect, test manager and data visualise are all required under the data science umbrella. The average salary for a data scientist was more than $111,000 in 2016, and the Bureau of Labour Statistics predicts that jobs in this field will grow by 11% by 2024

Acquire new skills with our Online Data Science Training Course

• Thorough knowledge of the statistical approach
• Master in concepts of Predictive Analytics using Python, and how they relate to practical approach.
• Machine Learning and how to Validate machine learning models 
• Experts in Data Visualization,
• Gain practical knowledge over Big Data and Analytics, etc

Prerequisites
This training course does not presume or require any prior knowledge or prerequisites. However, basic knowledge would be an added advantageous. We are recommending,  knowledge of programming languages like...

• Python
• Perl 
• C/C++
• SQL
• Java 
• And python skills that are required to become a data scientist

Course Overview

If you want to accelerate your career with Data Science certification program, world class experienced Trainers and faculty. Encourage yourself and become to master in trending data scientist skills including statistics, hypothesis testing, data mining. We are covering clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS Macros, recommendation engine, supervised and unsupervised learning and more. Our certification Program covers online instructor-led training classes including a lot of case studies, real time project working, and machine learning working concepts, expert guidance for certification. After completion of training candidate has expertise over concepts, mathematical computing, SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave, linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline. Mastering in concepts of recommendation engine, time series modeling, practical mastery over principles, algorithms and more..

Introduction to Data Science Online Training Course Curriculum

Introduction to Data Science

• What is Data Science?
• Data Scientists
• Examples of Data Science
• Python for Data Science

Data Analytics Overview

• Data Visualization
• Processes in Data Science
• Data Wrangling, Data Exploration, and Model Selection
• Exploratory Data Analysis or EDA
• Data Visualization
• Plotting
• Hypothesis Building and Testing

Statistical Analysis and Business Applications

• Introduction to Statistics
• Statistical and Non-Statistical Analysis
• Some Common Terms Used in Statistics
• Data Distribution: Central Tendency, Percentiles, Dispersion
• Histogram
• Bell Curve
• Hypothesis Testing
• Chi-Square Test
• Correlation Matrix
• Inferential Statistics

Python: Environment Setup and Essentials

• Introduction to Anaconda
• Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux
• Jupyter Notebook Installation
• Jupyter Notebook Introduction
• Variable Assignment
• Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
• Creating, accessing, and slicing tuples
• Creating, accessing, and slicing lists
• Creating, viewing, accessing, and modifying dicts
• Creating and using operations on sets
• Basic Operators: 'in', '+', '*'
• Functions
• Control Flow

Mathematical Computing with Python (NumPy)

• NumPy Overview
• Properties, Purpose, and Types of ndarray
• Class and Attributes of ndarray Object
• Basic Operations: Concept and Examples
• Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Copy and Views
• Universal Functions (ufunc)
• Shape Manipulation
• Broadcasting
• Linear Algebra

Scientific computing with Python (Scipy)

• SciPy and its Characteristics
• SciPy sub-packages
• SciPy sub-packages –Integration
• SciPy sub-packages – Optimize
• Linear Algebra
• SciPy sub-packages – Statistics
• SciPy sub-packages – Weave
• SciPy sub-packages - 10

Data Manipulation with Python (Pandas)

• Introduction to Pandas
• Data Structures
• Series
• DataFrame
• Missing Values
• Data Operations
• Data Standardization
• Pandas File Read and Write Support
• SQL Operation

Machine Learning with Python (Scikit–Learn)- Overview

• Introduction to Machine Learning
• Machine Learning Approach
• How Supervised and Unsupervised Learning Models Work
• Scikit-Learn
• Supervised Learning Models - Linear Regression
• Supervised Learning Models: Logistic Regression
• K Nearest Neighbors (K-NN) Model
• Unsupervised Learning Models: Clustering
• Unsupervised Learning Models: Dimensionality Reduction
• Pipeline
• Model Persistence
• Model Evaluation - Metric Functions

Natural Language Processing with Scikit-Learn- Overview

• NLP Overview
• NLP Approach for Text Data
• NLP Environment Setup
• NLP Sentence analysis
• NLP Applications
• Major NLP Libraries
• Scikit-Learn Approach
• Scikit - Learn Approach Built - in Modules
• Scikit - Learn Approach Feature Extraction
• Bag of Words
• Extraction Considerations
• Scikit - Learn Approach Model Training
• Scikit - Learn Grid Search and Multiple Parameters
• Pipeline

Data Visualization in Python using Chart JS 

• Introduction to Data Visualization 

â–º ChartJS

• Libraries 
• Chart JS Features
• Labels
• Data
• Datasets
• Controlling Line Patterns and Colours 
• Set Axis, Labels, and Legend Properties 
• Annotation 
• Different Types of Charts

Data Science with Python Web Scraping- Overview

• Web Scraping
• Common Data/Page Formats on The Web
• The Parser
• Importance of Objects
• Understanding the Tree
• Searching the Tree
• Navigating options
• Modifying the Tree
• Parsing Only Part of the Document
• Printing and Formatting
• Encoding

Student Take away

• Study Material
• Learning stuff
• Sample project for practice

Class Delievery 

• Live Interactive classes with expert

Delievery  Methodology
We are using an experiential delievering methodology that blends theoretical concepts with hands-on practical learning to ensure a holistic understanding of the subject or course