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:
- Developers aspiring to be a ‘Data Scientist’
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- ‘R’ professionals who want to captivate and analyze Big Data
- 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.
- 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.
|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|
|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|
|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|
|Exploratory Data Analysis||00:00:00|
|Visualization of Data||00:00:00|
|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|
|What is Random Forest?||00:00:00|
|What is Navies Bayes?||00:00:00|
|Support Vector Machine: Classification||00:00:00|
|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|
|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|
|Difference: User-Based and Item-Based Recommendation||00:00:00|
|Recommendation use cases||00:00:00|
|The concepts of text-mining||00:00:00|
|Text Mining Algorithms||00:00:00|
|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|
|Reinforcement learning Process Flow||00:00:00|
|Reinforced Learning Use cases||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|
|Variable Scope and Returning Values||00:00:00|
|The Import Statements||00:00:00|
|Why Dimensionality Reduction||00:00:00|
|Scaling dimensional model||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|