The population of the world is rising tremendously and most people started using the internet and other technology. So the device they use stores a large amount of data. Considering the huge population, there would be a massive amount of data. In recent years companies have started to make use of the data and develop what people want.

Industries started to work on obtaining data-driven insights to guide their future business strategy and execute plans for the same. Data in industry sectors like Finance, Medical etc are like the backbone of their services.

It's not a surprise that the demand for data-related jobs and opportunities across the globe is rising.

What is Data Science?

Data science is the field where it deals with a huge amount of data using modern tools and techniques like Linear regression, pattern recognition etc to find trends in that particular data and obtain meaningful insights to create an effective business model.

The process of Data Science :

1. Capture: The first step is to collect the data by a process like Data extraction, Data Entry.

2. Maintain: The data obtained should be assessed and cleaned for unwanted data and maintained in Data Warehouses.

3. Process: The third step consists of Data Modeling, Data Mining. Data Scientists analyse the data and derive various outcomes from it.

4. Analyze: This is the important process where they perform various analyses on the data. The process includes Regression, Text Mining etc.

5. Communicate: This is the final step where the outcome is represented in Reports, Graphs and other easily readable forms.

Some top skills to know to build a career in Data Science :

There are many courses, Workshops and Bootcamps in Data Science provided by various colleges and other sources. These will provide you with enough knowledge and develop the skills required for you to become a good Data Scientist.

The courses you take will provide you with good knowledge of Data Science and it will make you a good Data Scientist. But experience will take you to the position of a great Data Scientist.

Here are some of the concepts you should be thorough of :

1. Machine Learning: Machine learning is the division of Artificial Intelligence that makes a system perform a specific task automatically through programming. Hence a system that works without human intervention is created. In relation to Data Science, it is used to build predictive models. Since we have built a model it can work with new data and adapt to it. And also the predictions are based on both the new data and insights from the old data.

2. Algorithms: Algorithms are rules or steps used in the calculation to solve problems or perform a task. Many data models are accomplished with the use of various algorithms. These are used to generate results based on the data provided.

3. Statistical Models: Statistics play a vital role in data science. A good grip on statistics can help in obtaining more information and the same be represented effectively. These models can be used to extract information or predict the possible outcomes based on the data.

4. Regression Analysis: Regression analysis is a statistical process that estimates relationships between dependent and independent variables, to provide a real number value representing a quantity on a line.\

5. Programming: Programming languages can be used to create and enhance the models used in data analysis. These can be used to filter out junk, organise data and helps represent in understandable formats to the clients. Data scientists typically need to be trained in Java, Python, SQL, R and SAS. Additionally, they also require knowledge of Big Data frameworks such as Hadoop, Spark and Pig.

Various Professions in Data Science Domain:

There are various roles in Data Science. Here are some of the different ways you can explore the field:

1. Data Scientist: The job demands determining what the problem is, what the required answers and where to find the data. The skills needed are Programming skills like SAS, R, Python etc. They also need statistical and mathematical skills and some knowledge in Hadoop, SQL and Machine Learning.

2. Data Engineer: They focus on developing, deploying, managing and optimizing the organization's data infrastructure and data pipelines. They support data scientists by helping to transfer and transform data for queries. The skills needed are NoSQL databases.

3. Data Analyst: Analysts bridge the gap between the data scientists and Business analysts, organizing and analysing data to answer the questions the organization needs.