What can we do with this data, and why the courage of the people increasing towards the data related jobs in the industry. Is it a hype or reality.

What are the jobs in this field. Is it only Data Scientist or anything else. But going to that first of all what is Data?

You must have heard many buzz words related to this domain. But have you ever tried to get into deeper into these words?

In this session, we will discuss answering these questions and getting started in the world of Data.

Let us look briefly about these.

## What is Data?

In simple words, data is the raw information collected based on observation, surveys and experiments. Data can be numerical as well as categorical.

Data are a set of values of Qualitative and Quantitative variables.

## How can be Data useful for the enterprises?

Data can create value for businesses. Much useful information can be extracted from the raw data that helps enterprises to make decisions for implementation of new strategies and for creating new product and services. The most important step is to transform the raw data into useful information which can give some critical insights about the product and services of any business.

## What is Data Science?

Data Science is one of the sexiest jobs of this decade. But what is Data Science? Let us answer this complex question in very simple words. Data science is the extraction of actionable insights from data which can be used for the decision making for an enterprise.

The Science of Data in which we clean the data, organize it and explore it for getting useful information. Based on these explorations, building a Machine Learning model for prediction.

## Difference between Statistics and Data Science

There is a very famous term, Statistics, which is familiar to us. And it appears that both Data Science and Statistics are the same in terms of the steps performed in these, but what is the basic difference between a Statistician and a Data Scientist?

Statistics is the domain which concerns about collecting, organizing and analyzing the data based on mathematical interpretation. On the other hand Data Science is the multidisciplinary domain which concerns about statistics and based on the statistical exploration, predicting future trends.

## Real-world examples of Data Science

We can infer many real-world examples where data science is used. Some of the examples are predicting the sales, stock market prediction, time series analysis, predicting diseases in medical field, Maps giving us prediction about the traffic, targeted advertisement on social platforms such as Facebook and many more.

## Buzz words related to Data Science domain

We nowadays encounters many of buzz words used by everyone related to this data domain.

Some of them are- Data Science, Data Analysis, Business Analysis, Machine Learning, Business Intelligence, Data Engineering, Artificial Intelligence, Deep Learning etc.

What is the actual difference in these? How these are related to each other? What are the similarities in these?

We will go in-depth of all these buzz words and related topics in the upcoming sessions.

## Technical Knowledge required in Data Science

Can a person from non-technical background start his/her career in the Data Science domain? What are the technologies required?

List of technologies and mathematical knowledge is enough to dive in this domain-

1. Programming Knowledge

2. Probability and Statistics

3. Algebra and Calculus

4. Machine Learning

5. Business Understanding

In the upcoming sessions we will discuss about all these technologies and mathematical aspects in detail.

How much time do we need to be proficient in this domain?

The answer to this question depends on various factors such as your pace of learning, your IQ, your intelligence, your dedication and most importantly your resources of study.

Generally for a beginner 4-5 months are enough for diving into this domain and learning the concepts and application.

But as it depends solemnly on you that for what purpose you are learning Data Science, and how much time could you dedicate to it.

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