Be a Data Scientist Ready

Introduction

Data science is gathering a huge amount of popularity among business opportunities be at the corporate level or at a huge transition risk level. As IT changes with every decade the job functionality also changes, according to TechRepublic which mentioned that there were 56% high demand for Data Scientist job over the past year. So this evidence that how much the requirement for Data Scientist has grown over a few decades, and indeed it will grow due to large usage of data to deliver best practises to both consumer and operator. In fact, the job growth market for Data Science increasing rapidly, the survey by GlassDoor evidence the same.

Objective

Data Scientist - one who practises data Science in a good way to find solutions from huge data, makes use of data delivered to him/her to make sure both the parties, i.e customer and company can serve better, which makes the market a competitive one. The requirement for Data Scientist in companies are growing too, so here I will discuss 10 major points, that you should follow or learn to be a data scientist.

They are:

1. Basics 
2. Programming
3. Statistics
4. Data Visualization
5. Data Munging
6. Data Ingestion
7. Tool - Box
8. Data-Driven Problem Solving
9. Big Data
10. Machine Learning

Explanation

• Basics - Every great thing starts with basics, so does Data Science, well what I actually mean by basics, that includes the concept of matrices, ETL ( Extract Transform Load ), BI (Business Intelligence), Relational and Database concepts.
• Programming - Make yourself good in any of the programming language - suggestion -R or Python for Data Science.
• Statistics - Concept of Statistics should be cleared, and one can apply to in the programmatic method, like Skewness, Random Variable, Bayes Theorem, Cumulative Distribution Function (CDF), Probability, Explorative Data Analysis, etc.
• Data visualization - One should have a good grip over Data Visualization tools as Tableau, Kibana, Data wrapper etc.
• Data Munging- One should be able to understand and remove the data inconsistency which may result in an inappropriate result.
• Data Ingestion - Data Ingestion, in general, refers to the loading, processing data, importing or exporting for later use, some tools as apache Sqoop, and apache flume is good examples for Data Ingestion.
• Tool - Box - One might find it's useless, but it's really needed to be a great data Scientist as Tableau, Spark, MS Excel.
• Data-Driven Problem Solving - As a Data Scientist you should be able to optimise your algorithm, and ask yourself every time that do you really need that approach or algorithm, thus as=nswering your question.
• Big Data - Big Data information is really needed for getting things done easily for handling the huge amount of data.
• Machine Learning - Machine Learning practises as Supervised Learning, Unsupervised Learning or Reinforcement Learning are well appreciated, adding to the practices regression methods, decision tree, random forest are well applauded too.

Conclusion

The world is changing itself, so does the requirement and job description, Data Science is a huge field and it's a future technology, if one can practise itself to it's best he/she can become a great Data Scientist.