Data for Big data analysis using R, SAS, Python!

As the amount of data generated by the typical modern business increases, so does the prominence of data scientists hired by organizations to help them turn raw data into valuable business information. Data extraction is the act of retrieving specific data from unstructured or poorly structured data sources for further processing and investigation. Data scientists must possess a combination of analytic, machine learning, data mining and statistical skills, as well as experience with algorithms and coding. Along with managing and interpreting large amounts of data, many data scientists are also tasked with creating data visualization models that help illustrate the business value of digital information.

Learn from this comprehensive collection of case-studies, hand-picked by industry experts, to give you an in-depth understanding of how data science moves industries like telecom, transportation, e-commerce & more.

• Uber Supply Demand Gap
  Use analytics to identify why Uber sometimes faces a supply-demand challenge and what can be done to overcome it.
  Link : Uber Demand Supply Gap - Data Analysis
   
• Telecom Churn Prevention
  Telecom is an extremely competitive sector and the existing players face the constant challenge of customer churn. Learn how churning customers can be identified in this sector.Link : Churn in Telecom's dataset
   
• Spark Funds Investment
  Spark Funds is looking for new avenues to invest its surplus funds in the startup ecosystem. Help the fund identify the geographies and sector for its investment to maximise the return.
• Link : Investment Analysis Spark-Fund
   
• Retail Giant Sales Prediction
  Help a Retail giant predict the future sales volume of retail goods using time series forecasting so that it can plan the production and logistics better.
• Link :  Walmart Recruiting - Store Sales Forecasting 
   
• Creditworthiness of Customers
  Learn how predictive science analytics can be used to decide the creditworthiness of customers and whether they should be issued a credit card or not.
• Link :  Credit Card Clients Dataset
• E-commerce Market Mix Modelling 
  E-commerce websites often face the challenge of how much to spend on which marketing channel. Use modelling to help them figure out the optimal spend across channels to drive sales.
• Link : E-Commerce Data

You can use these data sets and apply operations on them using analytics tools like R, Python, Spark, etc.The benefits of data analysis tools are, essentially, the benefits of business intelligence. They vary tremendously depending on the individual case. Generally, however, they help identify, interpret and predict trends and patterns that affect the business.

Data analysis software can help, for example:

• Clarify the correlation between new marketing initiatives and improved sales
• Better predict customers’ needs by analyzing past purchases and browsing habits
• Improve internal workflows and suggest solutions to common bottlenecks

By extracting these kinds of meaningful insights from your data, you’re in a better position to understand what it will take for your business to increase profitability. When you analyze a data set, you’re reflecting on what is (or isn’t) running smoothly at your business. The great advantage of using software for this purpose is it’s often more reliable—and less time-consuming—than manual data coding methods.