Who is a Data Scientist & How to Become a Data Scientist

By | May 16, 2020
How to Become a Data Scientist

Data Scientist job is much in demand in this century as it is termed as the “hottest job”.

Although, it is an interesting job and requires a great amount of programming and analytical skills. So, what is this job all about? Who is a data scientist?


A data scientist is an analytical data expert who optimizes the growth of the organization by analyzing the business from its huge or massive high volume of data, cleaning it and then running certain AI and ML algorithms on the refined data sets to provide enhanced outcomes that prove to be beneficial for the growth of the organization.

There are certain steps that a data scientist follows to reach the ultimate goal of better decision making and thus providing strategic business moves.

The Stages are as follow:

  • Business Problem Understanding: The scientists are required to meet their clients and discuss end goals and targets to be achieved. The scientist must communicate well to understand the requirements and thus should be curious and ask as many questions as required.
  • Acquire Data: The next step is to collect data from numerous sources like webservers logs, APIs, etc.
  • Data Preparation: After the data is gathered, come data preparation which involves data cleaning and data transformation. Data cleaning is a time-consuming process and involves inconsistent data types, misspelt attributes, missing and duplicate values. Later, data is transformed based on defined mapping rules.
  • Exploratory Data Analysis: This step refines and defines the selection of feature variables that will be used in model development. This is a crucial step in the whole process.
  • Data Modeling: This is the core activity, which uses diverse ML techniques like KNN, decision trees, Naive Bayes to data to identify the model that best fits the business requirement.
  • Visualization: In this stage, the scientist meets the client again to communicate the business findings in a simple manner.
  • Deploy and Maintain: Test the model in a pre-production environment and then deploys it in the production environments. Then scientists use reports and dashboards to get real-time analytics and also monitors and maintains the performance.

Skill Sets Required to be a Data Scientist

  • Programming Skills: Being a data scientist requires you to be fluent with languages like Python, R, and Scala, other languages like C/C++ and Java could also be learned. Python is considered versatile for all the steps involved in data science. It can take any format of data and SQL tables could also be uploaded easily.
  • Databases and Frameworks: They contribute massively to handle huge volumes of data. Databases like SQL and Frameworks like Apache Spark, Hadoop are very much in demand in this industry.
  • Mathematics and Statistics: Mathematics is required to process and structure the massive data that data scientists deal with. The scientist must be good at linear algebra, calculus, and statistics. Statistics allows to play with data and eventually extract the insights to predict reasonable outcomes. A data scientist is expected to know how to use statistics to infer insights from smaller data sets onto larger populations.
  • Data Analysis: It becomes easy to contemplate data with data analysis and thus gives deeper and significant insights. Because of analysis, the market can be studied thoroughly and thus, leading to effective marketing actions.
  • Data Intuition: Companies expect you to be a data-driven problem-solver.
  • Machine Learning: There is a collection of machine learning algorithms to make predictions based on the data set fed.
  • Natural Language Processing(NLP)
  • Algorithms: Gain expertise in the following algorithms:
    • Linear Regression
    • Logistic Regression
    • Decision Tree
    • Random Forest
    • K Nearest Neighbor
    • Clustering (for example K-means)
  • Business Acumen: Data scientists not only work and analyze big amounts of data but also understand the intricacies of business organizations.

Tools that Aspiring Data Scientists Must Learn

  • Big Data Frameworks Tools:
    • HDFC: Storage part of Hadoop
    • Yarn: Performs resource management by allocating resources to different applications and scheduling jobs.
    • Map Reduce: It is a parallel processing paradigm that allows data to be processed parallelly on the top of HDFS.
    • Hive: This tool caters to the professional form of SQL background to perform analytics on the top of HDFS. Majorly used for creating reports.
    • Apache Pig: This high-level platform is for data transformation on the top of Hadoop. It is used for programming generally.
    • Scoop Flu: Tool used to import unstructured data from HDFS and import an export structured data from DBMS.
    • Zookeeper: it acts as a co-ordinator among the distributed services running in a Hadoop environment thus helping to configure management and synchronize services.
    • Suze: It is a scheduler that binds multiple logical jobs together and helps to accomplish a complete task.
  • Real-Time Processing Frameworks:
    • Apache Spark: This distributed real-time framework is used in the industry rigorously. It can be integrated with Hadoop easily leveraging HDFS as well.
  • DBMS and Database Architectures: A database management system stores, organizes and manages a large amount of information within a single software application. Thus, this helps to manage data efficiently and allows users to perform multiple tasks with ease. It also improves data sharing, data security, and data access and better data integration with minimizing data inconsistencies.
  • SQL Based Technologies: SQL is used to structure, manipulate and manage data stored in relational databases. Therefore, a strong command is required on at least one of the SQL based technologies listed below:
      • Oracle
      • MySQL
      • SQLite
      • IBM DB2
      • SQL SERVER
      • Postgre SQL
  • NoSQL Technologies: As the requirement of the organizations has grown beyond structured data, NoSQL technology has been introduced. It can store a massive amount of unstructured, semi-structured or structured data with quick hydration and adjoin structure as per application requirement. Some of the prominently used databases are:
      • HBASE: It is a column-oriented database that is great for scalable and distributed big data stores.
      • Cassandra: This is a highly scalable database with incremental scalability. The best feature of this tool is minimal administration and no single point of failure. Further, it is good for applications with fast and random read and writes.
      • MongoDB: This is a document-oriented NoSQL database. It gives full index support for high performance and replication for fault tolerance. It has a master/slave sort of architecture and is rigorously used by web applications and for semi-structured data handling.
  • Programming/Scripting Languages: Various programming language serves the same purpose so mastering one of the following is a must.
      • Python: highly recommended to learn.
      • R: This language has a steep learning curve and is developed by statisticians and generally used by analysts.
      • Java
  • ETL/DATA Warehousing: Data warehousing is crucial when the data is fed into heterogeneous sources ETL needs to apply. Data warehousing is used for analytics and reporting. This is important for business intelligence solutions. Following are the tools:
      • Talend: The major benefit of this tool is support from Big Data frameworks.
      • Qlik Q
  • Operating Systems: All mathematical tools are based on one of the operating systems listed:
      • Unix
      • Linux
      • Solaris

Career Path to Be a Data Scientist

  • Be Qualified: Many job descriptions mention the candidate to be a Masters or a Ph.D. in Computer Science, Mathematics or Statistics or Engineering. The other way to qualify for eligibility is by learning online. Various e-learning platforms have become a reasonable and efficient way to learn specialist skills that too at an affordable price.
  • Develop Technical Skills: Apart from the tools stated above, a candidate is also expected to have hands-on experience with some AI and Machine Learning tools and algorithms. Also, visualizing and presenting data with software or platforms such as ggplot, d3.js or tableau is a plus.
  • Non-Technical Skills: Apart from technical skills, a data scientist must possess the following non-technical abilities.
      • Attention to detail
      • Organizational skills
      • Problem-solving
      • Desire to learn
      • Resilience and focus
      • Communication and Teamwork
  • Build Your Portfolio: It is important to make an impressive first impression. Make a good quality resume or even better build a website to demonstrate your work and experience.
  • Build Network: Go to conferences and meetups to get exposure and stay updated with your field. There are a number of them but the ones that are highly popular are listed below:


        • The Strata Data Conference
        • Knowledge Discovery in Data Mining (KDD)
        • Neural Information Processing Systems (NeurlPS)
        • The International Conference on Machine Learning(ICML)


        • SF Data Mining
        • Data Science DC
        • Data Science London
        • Bay Area R User Group
  • Ace the Interview: There are a number of sites and blogs to help you with this. But the major requirement is that you must be well versed with the algorithms.

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