Course Content

The objective of Data Science course is to master study of data science to become a successful data scientist with Online/Live Classroom. The course is equipped with real time projects along with job assistance in global firms. The course primarily covers the complete range of SAS & R and Machine learning techniques as defined in the Data Science study.

The Data Science Certification Training enables you to gain knowledge of the entire Life Cycle of Data Science, analysing and visualising different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes. There are certain tools for carrying out massive data management, statistical modelling and provide algorithm for data mining such as clustering and associate rule mining to name a few.

Why this course ?

  • Businesses Will Need One Million Data Scientists by 2018 – Kdnuggets
  • Roles like chief data & chief analytics officers have emerged to ensure that analytical insights drive business strategies – Forbes
  • The average salary for a Data Scientist is $113k (Glassdoor)

Scope of the programme

After undertaking the course, one aims to achieve the proficiency in the following:

  • Understand the basic role played by the Data scientist in analysing the Data Analysis Life cycle.
  • Analyze Big data by the use of SAS and R statistically.
  • Learn Predictive Analytics, Machine Learning & Data mining Techniques
  • Insight in to various Machine Learning Techniques and their implementation using R.
  • Handling tools and techniques involved in sampling, filtering and data transformation
  • Work with different data formats like XML, CSV etc.
  • Learn tools and techniques for Data Transformation
  • Discuss Data Mining techniques and their implementation
  • Analyze data using Machine Learning algorithms in R
  • Explain Time Series and it’s related concepts
  • Perform Text Mining and Sentimental analyses on text data
  • Gain insight into Data Visualisation and Optimisation techniques
  • Understand the concepts of Deep Learning

Why Learn Data Science?

The incorporation of technology in our everyday lives has been made possible by the availability of data in enormous amounts. Data is drawn from different sectors and platforms including cell phones, social media, e-commerce sites, various surveys, internet searches, etc. However, the interpretation of vast amounts of unstructured data for effective decision making may prove too complex and time consuming for companies, hence, the emergence of Data Science.

Data science incorporates tools from multi disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, and some programming.

  • Data mining applies algorithms in the complex data set to reveal patterns which are then used to extract usable and relevant data from the set.
  • Statistical measures like predictive analytics utilize this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past.
  • Machine learning is an artificial intelligence tool that processes mass quantities of data that a human would be unable to process in a lifetime.

Who should go for this Course?

The following professionals can go for this course:

  1. Developers aspiring to be a ‘Data Scientist’
  2. Analytics Managers who are leading a team of analysts
  3. Business Analysts who want to understand Machine Learning (ML) Techniques
  4. Information Architects who want to gain expertise in Predictive Analytics
  5. ‘R’ professionals who want to captivate and analyze Big Data
  6. Analysts wanting to understand Data Science methodologies

What are the pre-requisites for this Course?

There is no specific pre-requisite for the course, however basic understanding of R can be beneficial. We offers you a complimentary self-paced course, i.e. “R Essentials” with this course.

Deliverables

  • Instructor-led Sessions
  • Real-life Case Studies- Live project based on any of the selected use cases, involving the implementation of Data Science.
  • Coverage- Comprehensive coverage on various analytical tools like R, SAS, Hadoop, Python, Tableau etc.
  • Advanced Analytics: Learn Text Analytics, Machine Learning, Marketing Analytics and Retail Analytics
  • Online Material: 24*7 access to practice material, videos, quizzes, mock tests, etc., to ensure learning efficiency.
  • 24 x 7 Expert Support- Expert faculty with wide industry experience in the Analytics industry and alumni of top universities
  • Mentoring: Get mentoring from data scientist working in leading companies such as Mckinsey, Deloitte, Mu Sigma, Google, PWC etc.
  • Lifetime Access- You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.
  • Placement assistance:Candidates will receive 100% placement assistance which includes interview grooming, resume writing etc.

Course Curriculum

Introduction to Data Science
What is Data Science? 00:00:00
What does Data Science involve? 00:00:00
Era of Data Science 00:00:00
Business Intelligence vs Data Science 00:00:00
Life cycle of Data Science 00:00:00
Tools of Data Science 00:00:00
Introduction to R 00:00:00
Introduction to Spark 00:00:00
Introduction to Machine Learning 00:00:00
Statistical Inference
What is Statistical Inference? 00:05:00
Terminologies of Statistics 00:00:00
Measures of Spread 00:00:00
Measures of Centers 00:00:00
Probability 00:00:00
Normal Distribution 00:00:00
Binary Distribution 00:00:00
Data Extraction, Wrangling and Exploration
Data Analysis Pipeline 00:00:00
What is Data Extraction? 00:00:00
Types of Data 00:00:00
Raw and Processed Data 00:00:00
Data Wrangling 00:00:00
Exploratory Data Analysis 00:00:00
Visualization of Data 00:00:00
Classification Techniques
What are classification and its use cases? 00:00:00
What is Decision Tree? 00:00:00
Algorithm for Decision Tree Induction 00:00:00
Creating a Perfect Decision Tree 00:00:00
Confusion Matrix 00:00:00
What is Random Forest? 00:00:00
What is Navies Bayes? 00:00:00
Support Vector Machine: Classification 00:00:00
Unsupervised Learning
What is Clustering & its use cases 00:00:00
What is K-means Clustering? 00:00:00
What is C-means Clustering? 00:00:00
What is Canopy Clustering? 00:00:00
What is Hierarchical Clustering? 00:00:00
Hands-On/Demo 00:00:00
Recommender Engines
What is Association Rules & its use cases? 00:00:00
What is Recommendation Engine & it’s working? 00:00:00
Types of Recommendations 00:00:00
User-Based Recommendation 00:00:00
Item-Based Recommendation 00:00:00
Difference: User-Based and Item-Based Recommendation 00:00:00
Recommendation use cases 00:00:00
Hands-On/Demo: 00:00:00
Text Mining
The concepts of text-mining 00:00:00
Use cases 00:00:00
Text Mining Algorithms 00:00:00
Quantifying text 00:00:00
TF-IDF 00:00:00
Beyond TF-IDF 00:00:00
Hands-On/Demo 00:00:00
Time Series
What is Time Series data? 00:00:00
Time Series variables 00:00:00
Different components of Time Series data 00:00:00
Visualize the data to identify Time Series Components 00:00:00
Implement ARIMA model for forecasting 00:00:00
Exponential smoothing models 00:00:00
Identifying different time series scenario based on which different Exponential Smoothing model can be applied 00:00:00
Implement respective ETS model for forecasting 00:00:00
Hands-On/Demo 00:00:00
Deep Learning
Reinforced Learning 00:00:00
Reinforcement learning Process Flow 00:00:00
Reinforced Learning Use cases 00:00:00
Deep Learning 00:00:00
Biological Neural Networks 00:00:00
Understand Artificial Neural Networks 00:00:00
Building an Artificial Neural Network 00:00:00
How ANN works 00:00:00
Important Terminologies of ANN’s 00:00:00
Function Parameters 00:00:00
Variable Scope and Returning Values 00:00:00
Lambda Functions 00:00:00
Standard Libraries 00:00:00
The Import Statements 00:00:00
Hands On/Demo 00:00:00
Why Dimensionality Reduction 00:00:00
PCA 00:00:00
Factor Analysis 00:00:00
Scaling dimensional model 00:00:00
Hands-On/Demo 00:00:00
What are Association Rules? 00:00:00
WHICH PROJECTS ARE INCLUDED IN THIS DATA SCIENCE TRAINING?
Project#1: Movies Collection 00:00:00
Project #2: Real Estate Price Prediction 00:00:00
Project #3: Diabetes Prediction 00:00:00
Project #4: Recommendation System for Grocery Store 00:00:00
Project #5: Twitter Analytics 00:00:00
Project #6: Air Passengers Forecasting 00:00:00
DATA SCIENCE COURSE CERTIFICATION PROCESS

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  1. 5

    Certybox is fantastic when it comes to giving professional training. The content and the online experience here made my learning experience so smooth and efficient that I was bound to recommend it to others. Go ahead without any hesitation. It will pay off.

  2. 5

    I enrolled in Certybox for an Online Self Learning course on Data Science Certification Training – R Programming. The LMS interface is very user-friendly and the course material is lucid and easy to understand. I have enjoyed my learning experience with Certybox.

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