Data Science : where the future lies
Today the question being faced by almost every organization is, how DATA can be used effectively — not just their own data but all of the available relevant data.
Data science is a field that comprises of everything that is related to data cleansing, preparation and analysis.It is umbrella of various techniques used when trying to extract useful insights and information from data.
Types of Data
Generally, data is categorized in three forms in data science as below:
- Structured Data (e.g. Excel, RDBMS)
- Unstructured Data (e.g. Satellite Images, Social media data)
- Semi-structured Data (e.g. XML, HTML,Email)
Where Data Science is used?
Data science is used in almost every field now-a-days.Some of the most common areas where it can be seen daily are:
1. Supermarkets
Supermarkets ask themselves the following kind of questions, to better arrange the products in the shelves:
a) Are people who purchase bread, also likely to purchase peanut butter and jelly?
b) Are families who enter the super market with kids likely to purchase candy?
c) Are people who purchase a swimming costume, also likely to purchase swimming goggles and a cap?
2. Social Networking
Every social network suggests and asks it’s users a lot of questions to improve their networking experience.
LinkedIn suggests/asks: “What jobs are you interested in?” or “What are the groups you like?”
Twitter suggests/asks: “Who would you like to follow?”
Facebook suggests/asks: “Who would you like to be friends with?” or “Which pages do you want to follow?”
Like these, there are uncountable examples where we can see presence of data science.
What skills you need?
Data science is intersection of many techniques.Some of them are listed below :
- Probability & Statistics
- Linear Algebra
- Machine Learning
- Computer Science
Hence, one needs to acquire skills in various components in order to harness the power of Data Science in today’s business world.
Data Science project life cycle
The ideal data science environment is one that encourages feedback and iteration among various stages of life cycle and it has been reflected in life cycle diagram of data science project.
In reality, the boundaries among the stages are fluid and the activities of one stage will often overlap those of other stages. Often, you’ll loop back and forth between two or more stages before moving forward in the overall process.
Takeaways
Excited to try it by yourself? Here is all you need to do:
Identify use case — Collect Data — Build Model — Train Model — Test Model — Deploy Solution
There are various platforms available where you can complete a data science project. For me, it was required to develop a system which suggests suitable predicted numbers to finance users to be entered as “expected upcoming revenue” based on their past entered data , for better forecasting. As goal and data was already clear and available to me, I had achieved my goal by implementing Linear Regression algorithm.
Define your goal and start building !!
Do or Don’t , there is no try ! — Yoda
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