Understanding of the key technologies in data science

 What Will You Learn ?

• Develop in depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics
• Practice problem analysis and decision-making
• Gain practical, hands-on experience with statistics programming languages and big data tools through coursework
• Employ cutting edge tools and technologies to analyze Big Data
• Apply algorithms to build machine intelligence

Who is the target audience?

• Anybody with an interest in Data Science
• Anybody who wants to improve their data mining skills
• Anybody who wants to improve their statistical modelling skills
• Anybody who wants to improve their data preparation skills
• Anybody who wants to improve their Data Science presentation skills

Benefits

• This course will give you a full overview of the Data Science journey. Upon completing this course you will know:
• How to clean and Prepare your data for analysis
• How to perform basis visualisation of your data
• How to model your data
• How to curve-fit your data
• And finally, how to present your findings and wow the audience

Salary

• According to Google, A data scientist earns around 1.2M$ per year

Requirements

• Only a passion for success
• All software used in this course is either available for Free or as a Demo version.

Introduction to Data Science

World & Data

It's certainly something that seems to be on everyone's mind lately.
But what is it ?
We'll find out together...
Have you ever wonder how,
Amazon recommends items for your to buy ?
Netflix recommends movies to you ?
Spotify recommends music to you ?

P.S. your every click on internet is contributing in data sets present all over.

When data is processed, organized so as to make it useful, it is called Information.

Earlier, data was limited and structured.

Today, the entire world is on the Internet !

And we have a variety of structured, understanding and semi structured data.

What do we mean we say structured and unstructured data ?

Suppose you are the principal of the institute.

You have a lot of data about all the students in your institute.

But your data is not sorted in any manner.

This can be considered as unstructured data.

You call you helper and assign him a task to create a single file per student.

That file should contain only that particular student's data.

Now that can be considered as structure data.

Structure Data can be stored in traditional database systems.

What are traditional database systems ?

Database in which data is stored in tables i.e. in the form of rows and columns is called as relational database systems or traditional database systems.

Unstructured Database cannot be stored in traditional database systems.

Fun Fact - Most of the Internet of Things (IOT) data is unstructured data.

Coming back to Data, Data is being produced constantly, every minute, every second with a very high speed !

Data Science is a blend of machine learning algorithms, statistics, business intelligence and programming.

It is helpful in discovering hidden patterns from the raw data.

Well done...

Hope you've got familiar with the word and world of DATA.

Problems solved by data science

Data Science can solve problems you'd expect it to.

For example - 

Netflix uses filtering algorithms to recommend you movies.

Many social media websites are powered by data science.

Construction of you Facebook's news feed, Or a suggestion of new people to follow on twitter,

Data science is widespread.

Websites like Zomato, Uber, Swiggy, etc. keep a vast amount of user data.

They do so to customize the user experience.

Data Science can also solve problems you'd never expect.

Urban planning has been made easier with data science.

With better understanding planners can factor in the required social facilities and amenities to support residents at a more localized level.

Number crunching (i.e. the work involved in bringing a numerical perspective to a situation.) plays a huge role in astronomy these days.

In fact data crunching plays a huge role in astronomy these days.

What is data crunching ?

• Data crunching is an overall term to cover the analysis of data so that it becomes useful in making decisions.

NBA teams are using data science to improve their training.

They have employed cameras that record movement of a player.

Later this data helps in new player's training and to get a competitive advantage.

Data Science: discovery of data insight

This aspect of data science is all about uncovering the findings from data.

It enables companies to make smarter decisions.

Let's look at the example of Netflix -

Netflix data mines movies viewing patterns, understands what drives the user interest, and uses that to make decisions on which Netflix original series to produce next.

Can you think of any other such example ?

Congratulations

You are one step towards being a Data Scientist.

How do data scientists mine out insights ?

It starts with data exploration.

When given a challenging question, data scientist become detectives.

They investigate leads and try to understand patterns or characteristics within the data.

Data Science: development of data product

A data product is a technical asset that:

• utilizes data as input, and
• processes that data to return algorithmically-generated results.

Gmail spam filter is a data product -

an algorithm behind the scenes processes incoming emails and determines if a message is junk or not.

This is different from the data insights section above.

In contrast, a data product is a technical functionality that encapsulates an algorithm and is designed to integrate directly into core applications.

Data Scientists play a central role in developing data product.

Data scientists serve as technical developers, building assets that can be leveraged at a wide scale.

Hopefully, you are clear with the concept of data insight and data product.

Data science is not simply a trendy new way to think about tech problems.

It is also a tool that can be used to solve problems in many fields.