How to become data scientist in 2018?

This article explains you the technologies you should learn to become data scientist in 2018.

How to become data scientist in 2018 and grab highly paid jobs?

Career in Big Data as data scientist is very hot career option these days. Skilled Data Scientists are in very high demand and this demand is expected to grow in coming years. Companies are looking for professional with right skills in Data Science. In this article we are going to list down all the necessary skills you must learn to become a successful Data Scientist.

1. Mathematical skills such as Algebra, Statistics and probability

Learn the Mathematical skills such as Algebra, Statistics and probability. These are used in developing Machine Learning programming models. You should understand these algorithms very well and apply it in writing Machine Learning programs. There are many programming languages and APIs for machine learning. You should have strong programming experience in these programming languages also.

2. Learn Big Data and Hadoop Ecosystem Components

Big Data technologies are used to manage and analyze vast amount of structured/unstructured data. Apache Hadoop is most used Big Data Platform in industry with many commercial distribution of Hadoop. Apache Hadoop is open source distributed storage and parallel computing platform which comes with many open source software developed at Apache Software foundation. These suites of open source software for Hadoop from Apache are know as Hadoop Ecosystem components. Apache Ecosystem components includes software like Pig, Hive, HBase, Flume, Spark, Sqoop etc.. These software stacks are used to develop robust data ingestion, cleaning, processing, storage and analytics system to suffice Big Data computing needs.

How to become data scientist in 2018 and grab highly paid jobs?

Among Hadoop Ecosystem components Apache Spark is most used software for parallel processing, machine learning, stream processing, real-time and batch processing. Apache Spark runs 100 times faster than MapReduce in memory.

3. Learn RDBMS and NoSQL Databases

If you are from programming background then most likely you must be knowing relational database. If you have experience in RDBMS then its plus or you. You can directly learn NoSQL database stacks such as HBase, Cassendra, MongoDB, Hive etc. For performing data analytics cleaned data is loaded from any of the database (RDBMS, csv file, excel file, NoSQl databases etc..) and analytics is done. So, Data Scientist must learn to use these various data sources and programming technologies used for accessing these databases.

4. Learn Programming Languages

Various programming languages are used to develop program for Machine Learning, Artificial Intelligence and predictive analysis. A good Data Scientist should have good exposure to programming languages. You should learn Java, Scala, Python, R and Spark programming languages. These are many API's available in each technologies which you can use. TensorFlow is also one of the highly popular framework for Deep Learning and artificial intelligence. You should also learn how to deploy machine learning models on the production environment. 

5. Learn Data visualization and reporting tools/technologies

Data Visualization and Reporting application development is an integral part of Data Science projects. Data Scientist performs analysis on data and come out with the meaningful results. These results must be presented to the business users and for this various reporting tools are used. Various data visualization formats and charts are used on the dashboard to show insight of data. So, as a Data Scientist you have to learn various data visualization tools such as Tableau, ClickView, QlikView, D3.js etc.. Sometimes these report generation work is also given to the UI developer or Visualization Analyst.

6. Practice with real projects and large sample data

Its very important to practice on real projects, you can take details of Big Data projects in different industry and download the sample data from free data sources. Its very important to practice with these projects. You will learn many things in trying with the real projects and try with various types of analytics.

7. Learn new technologies and keep yourself updated

Big Data and Machine learning technologies are fast updating with new programming techniques. You should subscribe for the Google Alerts or look for latest news and updates about changes in Machine Learning. This way you will be able to know about new development, news, tutorials and articles in this field.

8. Take part in data science competition

There are many places such as Kaggle where you can take part in programming competitions to keep yourself updated in Machine learning and deep learning technologies. Such websites are also a good place to learn new skills and get to know about new things. The credibility of any type of certificate from these online communities is very high in job market. You will get best offers through your profiles from these websites.

9. Join Data Science community

There are many online communities to discuss Data Science, Machine Learning, Artificial Intelligence and Neural Network technologies. You should join these communities and share your experience with them. You will also learn a lot of things from the experience of other guys as most of the time people publish their experience online in these community. Try to help other developers in their issue if you can. You will also find some material, experience etc.. on the latest top technologies through these communities.

10. Learn IoT and Data Lake Skills

IoT and Data Lake skills are going to be very high in demand in coming years. Companies are spending big budget on Big Data, Data Analytics and IoT technologies. So, you should be aware of these technologies and learn various programming languages used while application development. As a Data Scientist you many have to work on array of such technologies to get solutions developed and deployed to cater business requirement.

In this article we touched upon the technologies to be learned in 2018 to become successful Data Scientist.

Check more at Big Data tutorials, technologies, questions and answers.