HOW TO BECOME A DATA SCIENTIST?

WHAT IS DATA SCIENCE AND WHAT DO DATA SCIENTIST DO?

Data science is defined as the procedures and methodologies for data extraction, data analysis and to generate insights from structural and unstructured data.

Data scientists are analytical professionals who deal with large data and solve complex problems. They use advanced technological and professional skills to process, analyze and model large data to solve complex problems and generate analytical insights.

There are four pillars or fundamental areas of data science expertise:

1. Business Knowledge/Domain
2. Mathematics including Statistics and Probability
3. Computer Science and Software Programming
4. Communication (written and verbal)

DATA SCIENCE VS STATISTICS

Although both the fields have similar skills and use huge data sets to reach conclusions, they differ from each other based on their way of accessing the information. Data science uses computers and advanced technologies to analyse and model huge data to generate insights which help companies to take data-driven decisions.

Statistics is a mathematical field which uses theories and hypothesis testing to collect and interpret data and give conclusions.

STEPS FOR BECOMING DATA SCIENTIST

• EDUCATION REQUIREMENTS:

Professional fields like data scientists require high levels of education qualifications. The basic requirement is to have a college degree to launch a career in data science.

1. BACHELOR’S DEGREE:A four-year degree is the basic requirement to become a data scientist.

Bachelor’s Degree can be obtained in the following:

• Computer Science
• Maths
• Physics
• Statistics
• Social Science
• Applied Maths
• Economics

2.MASTER’S DEGREE:

• M.Tech in Data Science & Engineering
• PG Diploma in Data Science
• M.Sc. in Data Science
• M.Sc. in Big Data Analytics

These degrees are just a waste if one has no skill set to apply for data science. Some of the basic skills include coding, quantitative problem solving, managing large data sets, etc.

The online Certificate courses can also be very helpful in starting a career in data science. The E-learning platforms are helping individuals and professionals to upgrade their skill sets and gain in-depth knowledge at affordable prices.

SKILLS REQUIREMENT:

Without specialized skills and specific knowledge about data science the qualifications are not helpful in pursuing data scientist jobs.

There are two types of skill sets required for data scientists:

1. TECHNICAL SKILLS:

• PYTHON CODING: It is the commonly used programming language used by data scientists for coding. Since it is very versatile to use, it is used for almost all the processes of data science as it can easily import SQL tables into code. It is very useful to find any dataset in Google.
• HADOOP PLATFORM: It is very useful when the volume of data we have exceeds the memory of the system or when we have to send data from one server to another. This platform helps to transmit data to several points on a system. It is also used to explore data, filter data, sample data and summarize it.
• SQL DATABASE/CODING: Structured query language is a programming language which helps in operations like add, delete and extract data from a database and also helps in analytical functions and change database structures. It helps to give insights when a query is used in a database. It helps in time management and subdue the burden of large programming needed to perform difficult queries.
• APACHE SPARK: It is designed to help in running complicated algorithms faster. It saves time by distributing data processing when there is a huge set of data. It helps to prevent data loss and with its high speed of operation and user-friendly platform it helps in execution of complex unstructured data sets.
• MACHINE LEARNING AND AI: Machine learning skills like supervised machine learning, decision trees, logistic regression, etc. give a boost to the individuals who are pursuing to be data scientists. These skills help solving complex problems based on predictions of various outcomes.
• DATA VISUALIZATION: There are many data visualization tools like ggplot, d3.js and Matplotlib and Tableau which can help a data scientist to visualize data. It helps organizations to give insights about data by converting complex results from projects and help them to act accordingly towards new business opportunities.

2.NON-TECHNICAL SKILLS:

• Attention to detail
• Organization skills
• Problem-Solving
• Desire to Learn
• Resilience and focus
• Communication and Teamwork
• Intellectual curiosity
• Business acumen

• GAIN WORK EXPERIENCE:

Having work experience after graduation or during graduation helps to advance in future. There are many fields and sectors like healthcare, physics, business, manufacturing, retail, etc. where data scientists are required.

There are various projects which help to gain experience:

1. Cleaning Data
2. Creating Interactive Data Visualizations
3. Exploratory Data Analysis
4. Machine Learning

DATA SCIENTIST JOBS

There are many different types of data scientist jobs with different working environments but mostly prefer office type settings that allow people to work in groups so that they can work on the projects together and communicate easily. The work environment basically depends on the company or the industry which we work for. We can either work in a company which believes in quick results or in a company which values methodical and detailed progress.

Some of the sectors for data scientist:

1. Agriculture
2. Journalism
3. Education
4. Airline Industry
5. Image and speech recognition
6. Healthcare Industry

ADVANTAGES OF DATA SCIENTIST

• The employment rate of data science is very high.
• There are many opportunities for people with required skill sets.
• It is a very versatile field.
• They enrich data.
• They make products smarter and save lives.

DISADVANTAGES OF DATA SCIENTIST

• It is mostly referred to by critics as a re-branding of Statistics.
• Since the data science field keeps on upgrading and getting advanced day by day so no-one can actually master data science.
• It requires a huge amount of domain knowledge which makes it difficult to migrate from one industry to another.
• Most of the time while analyzing the data the privacy of the customer is at risk of being breached.
• The improper use of data for giving business insights and suggesting improvements can risk the business its projects.
• Organizations most of the time underestimate the complexity and time required to analyse the data so they often have very high expectations from data scientists.

Conclusion :-

In the era of digital transformation, in which every company needs highly qualified Data Scientist skills, several such institutes and colleges are providing some of the great knowledge-based resources for better technology growth. There are several opportunities to become a data scientist of this sort in the industry, leaving one extremely uncertain on what direction to take. Learnbay offers a course of professional instruction and complete assistance at a reasonable price, allowing candidates to get started in the profession quickly and efficiently.