Data science is quite a large and diverse field, getting a break into data science is very difficult. The title of the Data scientist in a company means a very different at every company.
So who is Data Scientist?
Traditionally data science focus on mathematics, computer science, and domain expertise, it means you have to be a jack of all trades but master of none initially because mastery comes with experiences.
Opting career as a Data Scientist probably the great career choice nowadays, you can read this article for more information.
Now we can break the skill of a data scientist in modern days as in our image also show. These are lots of skill and it will take a lot much time to learn but the good part is you don’t need to learn these so much skills.
Now comes to the main part and explain the individual skill category and make a roadmap to learn them,
Mathematics and Statistics: Mathematics plays a vital role in data science, with the help of mathematics we can identify pattern and create a new algorithm. The understanding of mathematics and statistics notations and concept are the key theories for implementing the different kinds of algorithms. There are some examples of notations and concepts of mathematics like regression, maximum value estimations, distributions, Bayes theorem etc, which play a vital role to implement the machine learning techniques.
Topic which is important in mathematics for data science
- Statistical Modeling
- Bayesian Inference
- Experimental Design
- Machine Learning
- Experimental Design
- Supervised Learning
- Unsupervised Learning
- Deep Learning
- Optimization
Computer science & Programming: Computer science plays a vital role in data science fields. Processing bulk of data into pipelines we need 80% of our working of collecting, cleaning and preprocessing and it requires a good knowledge of different programming languages, software tools, database concepts(both SQL and NoSQL), Big Data theories and working concepts, Cloud computing etc.
Topic which is important in computer science for data science
- Programming and
softwares tools - Statistics computing packages
- Databases(SQL and NoSQL)
- Parallel databases and parallel processing
- MapReduce concept
- Hadoop,Spark
- Cloud computing
Business and Domain
Topic which is important in business and domain knowledges for data science
- Knowledge about Business process
- Curious about data
- Problem solving
- Strategic,Proactive,Creative etc
Communication and Visualizations: Communicating and visualizing result is very important step in data science. If you want to be data scientist you should know the different visualization techniques and should also aware from different tools which is useful for visualizations.
- Able to engage with senior management
- Storytelling skills
- Translates data driven insights into actions
- Knowledges of different visualization techniques and tools.
These are the topics required to become a data scientist but there are lots of innovations happening on the daily basic, so one should also aware about the technologies trending and breakthroughs. Getting deeper into data science also require a proper plan to study the concepts and implementations the use cases.
Here in Hyperhack Lab, we are committed to providing the best study materials, use cases, concepts and research materials to make you more passionate about data science. We are also going to the learning path or roadmap of data science and Artificial intelligence.
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