Why Should I Learn Data Science with R from Pan Learn?
* This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree oveview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
* According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
* Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
* Randstad reports that pay hikes in the analytics industry are 50% higher than the IT industry
What are the course objectives?
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies.
* Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
* Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
What you will learn in this data science course?
This data science training course will enable you to:
* Gain a foundational understanding of business analytics
* Install R, R-studio, and workspace setup, and learn about the various R packages
* Master R programming and understand how various statements are executed in R
* Gain an in-depth understanding of data structure used in R and learn to import/export data in R
* Define, understand and use the various apply functions and DPYR functions
* Understand and use the various graphics in R for data visualization
* Gain a basic understanding of various statistical concepts
* Understand and use hypothesis testing method to drive business decisions
* Understand and use linear, non-linear regression models, and classification techniques for data analysis
* Learn and use the various association rules and Apriori algorithm
* Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
Who should take this Online Data Science Training Course?
There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this Data Science training particularly for the following professionals:
* IT professionals looking for a career switch into data science and analytics
* Software developers looking for a career switch into data science and analytics
* Professionals working in data and business analytics
* Graduates looking to build a career in analytics and data science
* Anyone with a genuine interest in the data science field
* Experienced professionals who would like to harness data science in their fields
Prerequisites: There are no prerequisites for this data science online training course. If you are new in the field of data science, this is the best course to start with.
What data science projects you will work on during this course?
The data science certification course includes ten real-life, industry-based projects. Successful evaluation of one of the following six projects is a part of the certification eligibility criteria.
Project 1: Products rating prediction for Amazon
Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.
Project 2: Demand Forecasting for Walmart
Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.
Project 3: Improving customer experience for Comcast
Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.
Project 4: Attrition Analysis for IBM
IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.
Domain: Workforce Analytics
A nationwide survey of hospital costs conducted by the US Agency for Healthcare consists of hospital records of inpatient samples. The given data is restricted to the city of Wisconsin and relates to patients in the age group 0-17 years. The agency wants to analyze the data to research on the health care costs and their utilization.
The data gives the details of third party motor insurance claims in Sweden for the year 1977. In Sweden, all motor insurance companies apply identical risk arguments to classify customers, and thus their portfolios and their claims statistics can be combined. The data were compiled by a Swedish Committee on the Analysis of Risk Premium in Motor Insurance. The Committee was asked to look into the problem of analyzing the real influence on the claims of the risk arguments and to compare this structure with the actual tariff.
A high-end fashion retail store is looking to expand its products. It wants to understand the market and find the current trends in the industry. It has a database of all products with attributes, such as style, material, season, and the sales of the products over a period of two months.
The web analytics team of www.datadb.com is interested to understand the web activities of the site, which are the sources used to access the website. They have a database that states the keywords of time in the page, source group, bounces, exits, unique page views, and visits.
An education department in the US needs to analyze the factors that influence the admission of a student into a college. Analyze the historical data and determine the key drivers.
A UK-based online retail store has captured the sales data for different products for the period of one year (Nov 2016 to Dec 2017). The organization sells gifts primarily on the online platform. The customers who make a purchase consume directly for themselves. There are small businesses that buy in bulk and sell to other customers through the retail outlet channel. Find significant customers for the business who make high purchases of their favourite products.
The course also includes 4 more projects for you to practice.
Details of listener preferences are recorded online. This data is not only used for recommending music that the listener is likely to enjoy but also to drive a focused marketing strategy that sends out advertisements for music that a listener may wish to buy. Using the demographic data, predict the music preferences of the user for targeted advertising.
Domain: Music Industry
You’ll predict whether someone will default or not default on a loan based on user demographic data. You’ll perform logistic regression by considering the loan’s features and the characteristics of the borrower as explanatory variables.
Analyze the monthly, seasonally-adjusted unemployment rates for U.S. employment data of all 50 states, covering the period from January 1976 through August 2010. The requirement is to cluster the states into groups that are alike using a feature vector.
Flight delays are frequently experienced when flying from the Washington DC area to the New York City area. By using logistical regression, you’ll identify flights that are likely to be delayed. The provided dataset helps with a number of variables including airports and flight times.