How to become data scientist and analyst – big data analyst

Data science jobs require you to be versatile so that you can adapt to the various techniques used to analyze and solve problems. Data science jobs are available in several sectors in information science, computer science, health care, and business. One can choose from different fields of Data Scientists based on their interest or even geographical location. There is a need to implement predictive methods that use big data to make inferences about future trends in today’s economic scenario. Thus data science jobs have become a necessity for every organization irrespective of its size, which is also evident from the growing demand for graduate jobs in this field.

What skills do you need to become a Data Scientist?

Required skills to become a Data Scientist

1. Data Analysis

To become a better data scientist, one should be thorough with Data Analysis.
One must learn these skills:

  1. Data science with R: R consist of various libraries and packages that help in the statistical analysis of data, visualization of the data, manipulation of the data, and to perform exploratory data analysis. It also consists of various packages that help in implementing machine learning algorithms. It is the highly recommended language by the professional and statisticians for various tasks of data science.
  2. Data science with Python: Python is widely used for various development processes such as Web Development, Application Development, and it is comprehensively used in the field of Data Science, Machine Learning, Artificial Intelligence because it consists of numerous various packages and libraries such as Pandas, NumPy and various framework that helps in data visualization, manipulation, exploratory data analysis and also to build machine learning algorithms and models that helps in automation.
  3. Mathematics: Mathematics is a fear of many students. But if you want to be a good data scientist, you must be very good at statistics. You have to be fast. Calculus and linear algebra also play a vital role but not as much as statistics.

2. Data Visualization

It depends upon the organization, whether they are comfortable with R or Python. Most of the organization uses R for data visualization. Some use Tableau and SAS. SAS is mainly used by companies that build high-end software. SAS is a paid tool that consists of various special features for statistical analysis and visualization of the data.

3. Processing Big Data by Big Data Analyst

This massive amount of data is known as Big Data. For processing and analyzing Big Data, various tools are used by an analyst, such as:

  1. Apache Hadoop (Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation.)
  2. Apache Spark (Apache Spark is an open-source distributed general-purpose cluster-computing framework.)
  3. Apache Kafka (Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.)
  4. Scala (Scala is a general-purpose programming language providing support for both object-oriented programming and functional programming. The language has a robust static type system.)

More on processing of Big Data

4. Machine Learning, Artificial Intelligence, Deep Learning

So in this, one should be aware of various machine algorithms under the category of classification, regression, clustering, and boosting algorithms.

It completely depends upon the organization in which you are willing to work, which domain you prefer.

Different sectors of Data Science

If it is a Health Care Firm, one must be aware of these blustering, classification, regression, and boosting algorithms or so that one can classify the data of the patient.

If you are willing to work in a Mobile Phone Company where you will be involved in developing mobile phone software, you must be aware of NLP concepts and implement them. Nowadays, mobile phones are coming with voice recognition technology and voice over search technology, so you must be mindful that you are willing to join a mobile phone company to develop mobile phones’ software.

Suppose you are willing to go into the Automobile Industries that are developing self-driving cars. In that case, you must be thorough with neural networks and improve neural networks as all the automobile industries that are growing self-driving vehicles rely on neural networks to ensure a safe drive.

Try exploring each tool one by one. And keep practicing. You will surely achieve the milestone.

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