Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges. <
What is this course about?
This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
Who will benefit from this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. 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. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
After completion of this training course, you will be able to:
This training has a clear focus on the vital concepts of business analytics and R. By the end of the training, participants will be able to:
1. Work on data exploration, data visualization, and predictive modeling techniques with ease.
2. Gain fundamental knowledge on analytics and how it assists with decision making.
3. Work with confidence using the R language.
4. Understand and work on statistical concepts like linear & logistic regression, cluster analysis, and forecasting.
5. Develop a structured approach to use statistical techniques and R language.
6. Perform sharp data analysis to make business decisions.
What background do I need?
The pre-requisites for learning 'Mastering Data Analytics with R' include basic statistics knowledge. We provide a complimentary course "Statistics Essentials for R" to all the participants who enroll for the Data Analytics with R Training. This course helps you brush up your statistics skills.
I am from a non-technical background. Will I benefit from this course?
Yes, the course presents both the business and technical benefits of Big Data analytics and Data Visualization. The data mining and technical discussions are at a level that attendees with a business background can understand and apply. Where technical knowledge is required, sufficient guidance for all backgrounds is provided to enable activities to be completed and the learning objectives achieved.
Which Case-Studies will be a part of the Course?
Towards the end of the Course, you will be working on a live project. Here are the few Industry-wise case studies e.g. Finance, Retail, Media, Aviation, Sports etc. which you can take up as your project work:
Project#1: Flight Delay Prediction
Industry : Aviation
Description : The goal of this project is to predict the Arrival Time of a flight given the parameters like:"UniqueCarrier", "DepDelay", "AirTime", "Distance", "ArrDelay", etc. Whether these attributes affect the arrival delay and if yes, to which extent? Construct a model and predict the arrival delay.Compute the (Source Airport - Destination Airport) mean scheduled time, actual and inflight time with the help of MapReduce in R and visualize the results using R.
Project #2: Stock Market Prediction
Industry : Finance
Description : This problem is about making predictions on the stock market data.The dataset contains the daily quotes of the SP500 stock index from 1970-01-02 to 2009-09-15 (10,000+ daily sessions). For each day information is given on the Open, High, Low and Close prices, and also for the Volume and adjusted close price.
Project #3: Twitter Analytics
Industry : Social Media
Description : This problem is about social media analytics. This can be defined as Measuring, Analyzing, and Interpreting interactions and associations between people, topics and ideas. The dataset to be analyzed is captured by Live Twitter Streaming. This problem is mainly about how to use twitter analytics to find meaningful data by performing Sentiment analysis of the tweets obtained and visualizing the conclusions.
Project #4: Recommendation System
Industry : e-commerce
Description : The problem of creating recommendations given a large data set from directly elicited ratings is a widely potential area which was lately boosted by players like Amazon, Netflix, Google to name a few. In this project, you are given a collection of real world data from the different users involving the products they like, rating assigned to the product, etc. and you have to create and come up with recommendations for the users.
Project #5: NFL Data Analysis
Industry : Sports
Description : The dataset is a set of tweets by fans from a NFL game. This project is about analyzing the tweets posted by football fans all over the world on the NFL tournament semi-finals and find out insights like: top 10 most popular topics being discussed, most talked about team etc.
Hands on Training with Practical using of different tools ...IT
Apex technology focus on developed single application that ...IT
This training will have Case Study, Hands-on LAB ...IT
Practical examples, exercises & case studies with ...IT
Md. Rabiul Islam
Md. Rabiul Islam has been working with Business Analytics & MIS, Process Reengineering & Automation, Business Research & Development, Project Management, OpenERP customization based on Clients’ Requirement.
He has completed his Graduation on Computer Science and Engineering from the University of Dhaka. He has served Dhaka University Library Management Project, Dhaka University Admission Project. He has been involved with some Research Based work on FMDV Modeling and BT bio-pesticide Toxin and TSS Prediction.
His major accomplishments and Contributions are: Big Data, Data Mining, Semantic Web and Machine Learning.
He has been a reputed public and corporate trainer for several years on subjects like python, Bioinformatics Algorithm using Python, Django frame work, Java, PHP.