what is the scope of data science program

you will develop expertise in multiple areas example

databases

user interface (animation etc.)

artificial intelligence

server logic etc.

Data science is huge and there are different areas you can get your hands into depending on your interest. Here are different career path you can take in data science:

1. Data /Business analyst: Here you will help business people in understanding the problem and diving deep into how it occured. You will be working on Excel, SQL and little bit of coding. People take this role as they love data, business and presentations but don't like coding.
2. Data scientist: This is bit of technical role but you need to understand business well. Here you are proficient in statistics, machine learning, visualization, SQL and (Python or R). Data scientist job is to answer business problem like why it happened and how can you improve or optimize things. For eg: Improve sales of a product, improve marketing, find reason why customers are churning etc
3. Machine learning Engineer: This is like a software engineering position which is heavily based on coding and managing code. You will need to have strong background in Machine learning, Coding, project / product management etc.

Data Science is has a very broad scope. Think about it in this way... With quantitative information, analytics and or metrics, it allows anyone whether you are CEO, your own business owner, or utilities company to make informed decisions to the betterment of their business. An example of this is a basic metric in a software company. They continue to get support inquiries on a How to setup on some configuration. After maybe the 50th same question, teams can write algorithms to highlight this as a Problem that can be mitigated thru creation of a guide. Not only does this help case reflection it also helps with case reduction. So back to your question... what is a scope of a data science program. It is the study of how to study data, apply business logic against them, it will require learnings on how to write data queries, pivot data, summarize, and highlight potential gaps in features, it will also require a lot of statistical analysis. There is a very structured way to analyze data (Hypothesis, regression analysis of business decisions, machine learning, Extrapolating sample data, etc...). With the recent progression of AI and machine learning, Data Science programs is the future. Being able to identify a problem area and what is plaguing a company and recommend how to tackle those solutions is in high demand. There are new roles popping everywhere similar to the CEO such as Chief Data Officer!

Hi,

I have been working in data science for over 10 years at various large and small companies. I am very passionate about this area.

The way I think about it is there are really 2 parts to data science:

1. Domain Expertise, this is really all about the problem you are trying to solve. This can be testing a new drug, understanding capacity in a data center, understand who responds best to what email message, etc.
2. Data, this is all about what data do I need to understand, describe or get some insights into the problem you identified above.

That means you need to have some business understanding and be very passionate about data. For example someone says I think Electrical Vehicle have no future, your job as a data scientist is to find relevant data and let the data tell you if that is a true or not. You will need to defend your answer with the data you discovered.

Now to the core of the data problem, what skills do you need?

1. You need to be able to extract data, that could be from a database or web service, etc. Most likely some SQL skills come in handy.
2. You need to understand that data, is it useful, complete, etc? This is called data profiling, you look at distribution of values, so you understand the context of the data.
3. At this point you like to explore the data, that can be done in a lot of different ways. Charting, filtering of data, etc. I use a lot of Tableau where you can download a trial. Excel is a must in this area or use Google Spreadsheet.
4. Apply more advanced methods, e.g. building a statistical model or data mining (also referred to as AI today).
5. Summarize your findings so a non-data scientist can actually understand what you found.

The space of data science is huge and covers many different problems, professional roles, and career trajectories.

As there is a lot of scope for a career in data science, the knowledge of these languages play a major role in building your Career in Data Science. Programming is a must in all the fields these days. Especially when you are dealing with data. But having knowledge only in programming don't yield you much. To consider this, let's take a look at the general question that might arise. I would suggest go for some online courses.

Skills You’ll Need: Knowledge of algorithms, statistics, mathematics, and broad knowledge of programming languages such as R and Python. Broad knowledge of how to structure a data problem, from framing the right questions to ask, to communicating the results effectively.