Data science overview

Data science full course for free

Hi, I am Deepak Goyal, IBM certified data scientist. In this blog, i will share you complete knowledge of data science that is you need to know to get expertise in data science. At last, I will tell you how to get a job. I assured you I will provide all the study materials that you need in a sequence. In case if you need personal help then just comment on my blog I will reply as soon as possible. So let's get started.
course contents:
1. what is Data Science?
   under the first chapter we will cover:
 1. Defining Data science and what is the work of data scientists.
 2. What needed to be a Data scientist
 3. Data science topics
 4. Data science in business
 5. A career in Data science
2. Toolbox for a Data scientist.
  1. Jupyter Notebook
  2. Apache Zeppelin Notebook
  3. R Studio IDE
3. Statistics and probability
4. Python Fundamentals 
5. Python For Data science 
6. Python for Machine Learing
7. Machine learning Algorithm 
8. Deep learning 
9. Neaural Networks.
We will cover all topics in Details. Lets look on first chapter:
                                               Chapter -1
                                     WHAT IS DATA SCIENCE 
Data Science is a interdisciplinary branch that mean It does not Require any pre expertise in any domain.Any one can become expert in this subject. If belongs to engineering, non engineering background then even you are eligible. 
So Data science is "what Data scientist Do". you may confused with this definition but in actual it is perfect definition of Data science. However If you want long definition then you might say Data science is the field of exploring and analyzing Data , and using data to answer question or make recommendations. So this is something that Data scientist Do. 
Harvard Business Review called Data science"The sexiest job in 21st century."
And average annual package of USD 120000. 

Fundamentals of Data Science :

Data Science can help organizations i n the following way ;

1. Understanding their environments

2. Analyze existing issues

3.Reveal Previously hidden opportunities

4. Powerful Data visualization Tools

5. understand the nature of the Results

6 Recommended action to take. and many more .

What needed to be a Data Scientist:

As Data science is interdisciplinary branch so any one can enter into this field. Let me give you some examples of the experts who were from different backgrounds but now they are hero in Data science.

Shingai Manjengwa  CEO of Fireside Analytic Inc , Her UG and PG was in political and economics , there is no relation between political and data science. But she is expert in Data analytics .

Murtaza haider , He is assistant professor Ted Rogers school of management, he was civil engineer.

and I have majored my undergraduates in Mechanical Engineering. So now is there any thing which need to be good data scientist.

Answer is Yes, you should be have 3 qualities:

1. curiosity 

2. fluency in analytics

3. you should be good story teller that is you should be Argumentative . 

IF you have these qualities then you are welcome to the world of Data science. because all these qualities can not be teach by someone. 

Terms of Data Science :

1. Big Data 

2. Machine learning

3. Data mining

4. Deep Leaning

5. Neural Network etc.

lets discuss all terms one by one 

Big Data :

Big data is analyzing vast data. Big Data is defined by v's.

The v's of Big Data are following:

1. velocity : The speed at which data accumulate .

2. Volume: Volume is the scale of Data or the increase 

the amount of Data stored.

     Drivers: Drivers of volume are the increase in Data sources , 

High resolution sensors and scalable infrastructure .

All these drivers helps in storing more and more Data.

3.Variety : Variety is the diversity of the data .

    variety also reflects that the data comes from different sourses,

machines, people and processes both internal and external to the organizations.

Drivers :Drivers of variety are mobile technologies, social media, wearable technologies and geo technologies videos etc.

4. Varacity: varacity is the quality and origine of data and its conformity to facts and accuracy.

5. Value: Value is our ability and need to turn data into value. It is not just profit but sometimes it may have medical and social benefits as well as customer , employee and personal satisfaction 

so this is all about v's of Big Data.

Data mining :

It is a process of automatically searching and analyzing data .

• Discovering Previously unrevealed patterns.
• It involves Pre processing the data to prepare it and transforming it into an appropriate formet.
• Once this is done, insights and patterns are mined and extracted using various tools and techniques ranging from simple data visualization tools to machine learning and statistical models. 

Machine Learning:

A subset of Artificial intelligence that uses computers algorithms to analyze data and make intelligent decisions based on what it has learned , without being explicitly programmed. 

     machine learning algorithms are :

•  Trained with large data sets 
• They learn from examples
• They do not follow rules based algorithms.

    Machine learning enables the machines to solve problems to their own and make accurate predictions using the provided data.

For example: In youtube and Netflix we get suggestions based on our search history. This is nothing but machine learning. Here machine learns from your searching data and provide you suggestions.

Deep learning:

Deep learning is a specialized subset of machine learning that uses layered neural networks to simulate human decision making. 

      Deep learning algorithms can : 

• label and categorize informations and identify patterns .
• It enables Artificial Intelligence systems to continuously learn on job and improve the quality and accuracy of results by determining whether decision were correct.

Neural Networks :

Neural Networks take insipiiration from biological neural networks , although they work quite a bit differently.

 A neural network in AI is collection of small computing units called neurons that takes incoming data and learn to make decision over time.

This is all about Data science terms. In consequent chapters we will study all these terms at broad level.

Next chapter will be coming soon....If you like my content then share it with your friends.