Introduction to Data Science

Introduction to Data Science or Business Analytics:

1. Meaning of Business Analytics
2. Evolution Of Business Analytics
3. Different Types Of Analytics
4. Application Of Business Analytics

Need or use of Business Analytics:

Before you know about Machine Learning or Data Science or Business Analytics, you have to know the need or use of Data Science. Day to day base different organism tries to solve different business portion for their growth.

Different business portion means

• How much you stock in inventory? 
• Which seller may miss the target of order? 
• Which factor can influence customers preference? 
• What kind of customer’s sentiments is there about your product? 
• What would be the next demand of the customers? 

All those business-oriented queries can be solved in two different way -Traditional Approach & Data Driven.

Traditional Approach: 

The data-driven is a decision which is adopted by all the organization in today’s world., here you can hire a senior one who has a lot of statistical knowledge about their traditional way of business. 

Data Driven:

All the company collect some data then store the data and processing it thereafter they analyze the data from multiple sources in a lot of statistical technique, while complete the realization then it shows in dashboards and reports.

That is the way data driven to insight, here it is like a roadmap where data is collected to insight. In these process, the analyzing process is most vital that’s why we ultimate focus on the analytical part. 

Meaning of Business Analytics:

1. Business Analytical(BA) is the practice of interactive, methodical exploration of an organization’s data through statistical analysis.
2. Business Analytics is used by companies by accepted to data driven decision making.
3. All organization in every industry focus on exploiting data for competitive advantages.

Evolution of Analytics:

In 2008-2010 there was a huge change in data.There was a lot of volume of data like tweets, facebook post , youtube videos That’s why to store the data we need a cheap commodities hardware and the answer is Hadoop where we can store all those data.Then we find out  MapReduce. Using the MapReduce technique we process those data in a fraction of second. 

You can see the trends in last ten years the cost of storing the data is gradually decreased

And the processing power is increased in last decade. 

Now combining these two factors we can store a data and analyze it.

Now the third stage is about machine learning. Using machine learning technique we can understand a data and extract the insight from data.

For an example,Decision Tree and Neural Network these two are one of the machine learning algorithms ,invented in 1970’s but at that time if you  ran a decision tree of 1gb of data it took half an hour but in today’s world  with Hadoop,it  took just one second.These are the three reason because of that we are using it.

Types of business Analytics:

1. Descriptive Analytics
2. Diagnostic Analytics
3. Predictive Analytics
4. Prescriptive Analytics

The graph Descriptive to Prescriptive, the complexity is higher as well the business value and skill also high.

Descriptive Analytics:

If you know more about how something happened or if you know more about a business event happened or you interested to know what happened or those kinds of think that is Descriptive Analytics.

• As it requires minimal to no coding, that’s why Descriptive Analytics is the easiest technique for data analytics.
• It is analysis of the past(historical) data to understand trends and evaluates metrics over time.
• There are many sophisticated tools that can handle Descriptive Analytics like Tableau,QlikView,Microstrategy, Google Analytics etc. 

EXAMPLE:

1. Analyzing past 6 months sales data and identify top 10 selling product
2. Analyzing customers comment on Twitter and count positive and negative comments

Diagnostic Analytics:

Thereafter if you go one level down and think why it happened in the past then it’s called diagnostic Analytics. 

• It involves the technique  why it happened
• By using statistical methods and basic data exploration we can understand the reason behind happening some event.

EXAMPLE:

1. Analyze why the sales were down in a particular region
2. Analyze why customer leave your organization

Predictive Analytics:

This Analytics said what would be happened or what might be happened. Predictive Analytics, the more complex which gives you more business value. 

• These analytics predict the future outcomes.
• It can also predict the impact of a variable (weather) on another variable(sale).
• It can be future categorized into different technique such as-Predictive Modelling,Data Mining, forecasting.

1. EXAMPLE:
   1. Predictive Analytics are Predictive future sales based on past historical data
   2. Predictive whether a customer would take a product or not
   3. Predicting whether a customer leaves your organization or not.

Prescriptive Analytics:

The final one is prescriptive Analytics,it tells what would happened and it also tell what you have to do,just like a Doctor prescription.

• Prescriptive Analytics specifies best course of actions for a business activity in the form of the output of a prescriptive model.
• It uses optimization algorithm to create the final output or prescription. 

The Prescriptive Analytic is sophisticated tools and technique as well.

EXAMPLE:

1. Supply chain- Finding best route to deliver the product
2. Marketing-optimum budget for marketing expenditure in each channel
3. Retail also-use price markdown model that provide the when to give discounts or offers and how much to give discount 

Decision Automation:

Using these four process, we can automate the whole process. but how?

We are dealing with lots of data using some technique and based on that we are taking a decision. 

In Descriptive Analytics then the human or the marketing manager or you have to take on decision based on the output from your Descriptive Model. 

Thereafter Diagnostic Analytics,may be the human input is a bit less but still, you have to take the decision based on the output.

Then predictive you take the decision based on the data or based on the predictive output. 

Now, the prescriptive model you don’t need to do anything everything is providing the model you just simply take the decision.