Ten years ago the role of Data Scientist did not exist and yet Data Scientist has become the most sought after job of this century with huge demand, millions of opportunities and high salaries. This is due to the generation of huge business data, which has immense potential to provide valuable business insights.

The job of a Data Scientist is to collect this data from various sources, clean, transform and prepare the data, and then use statistical and machine learning skills to extract business insights from the data.

In this article, we will discuss the prerequisites, required skills, proven learning approach, job readiness, and time frame to become a Data Scientist.

What are the prerequisites to become a Data Scientist?

The role of the Data Scientist is too generalized. In practice, the role of a Data Scientist can vary from basic research work to application data science techniques to data for business information.

While a data scientist, who works on basic research areas and develops algorithms and optimization techniques, needs a strong statistical and mathematical background, it is NOT necessary for a data scientist typical, who transforms data into business information, to have strong mathematical knowledge or programming skills.

The majority of data science roles require applied knowledge in statistics, mathematics, data management, and machine learning, as well as respective business/domain knowledge.

There are NO strict prerequisites as such, but it is recommended to acquire basic skills in the main areas of data science, including, Python ProgrammingBasic Mathematics, Statistics and Exploratory Data Analysis, before venturing into the field of Data Science.

What are the skills required to become a Data Scientist?

Data science is multidisciplinary and includes programming, math, statistics, machine learning, and business/domain knowledge.

The technical skills required for data scientists include:

1. Programming – Python (Core Python, Essential Data Science packages, etc.)
2. Mathematics (linear algebra, probabilities, derivatives, matrices, etc.)
3. Statistics (descriptive data analysis, sampling, probability distributions, hypothesis testing, etc.)
4. Data collection and cleaning
5. Data visualization and exploratory data analysis.
6. Machine learning modeling and model deployment

Soft skills include:

1. Analytical Curiosity – Question everything.
2. Data Intuition – Making sense of data
3. Solid understanding of business
4. Effective communication

Soft skills are what differentiate a good data scientist from an average data scientist.

What is the best approach to mastering data science?

Well, there is no best approach to learn data science as it varies depending on the learner profile. For someone starting from scratch, it will take an average of 6 months to earn enough data science skills.

DataMites®, a leading data science institute, has successfully trained over 50,000 learners and helped thousands of people transition to a data science career. The DataMites® adapts one of the best data science learning approaches, which basically has 3 phases.

Phase 1: Learn key data science skills

The first is to start by acquiring key skills in data science, from basics to advanced levels. This is the most demanding phase as you have to put rigorous effects into learning new concepts, practicing and learning to apply.

Data Science Fundamentals ⇒ Python ⇒ Statistics ⇒ Exploratory Data Analysis ⇒ Data Visualization ⇒ Machine Learning ⇒ Model Optimization ⇒ Data Science Model Deployment

Phase 2: Practice Capstone projects in data science

Data Science Capstone/learning projects are very important to master the new concepts you have learned. There are many sources for practicing projects and kaggle.com is the most popular.

It is advisable to practice at least 10 projects in each type of machine learning, classification, regressions, clustering and recommendations.

Phase 3: Real-Time Data Science Project

Unless you add value to the business with real-time projects using data science and machine learning, you cannot be called a data scientist. Real-time projects can be from a small business, a proof-of-concept project for a large client, a start-up project, or a product idea.

This is very important for appreciating data science as a field and understanding the value it could add to the business. Moreover, it will have a significant importance in your profile when looking for data science jobs to meet real challenges.

How to prepare for a job in Data Science?

Preparing for the job requires more than acquiring data science skills.

Job Role:

The field of data science has many roles ranging from technical, functional, business and leadership roles. The first step is to select a Data Science Job Role according to your profile and your areas of interest.

RESUME:

The CV makes the first conversation, so it is crucial to refine the CV by prioritizing relevant and important skills, highlighting technical skills, mentioning project details with your specific contribution, etc.,

Typically, the first level of filtering happens automatically with software robots, so it’s important to keep the right keywords matched to job descriptions.

Preparing for the interview:

Job interviews require totally different preparations from presentation, communication, concept brushing, etc.

For instance:

1. While it’s not that important to remember key concepts, definitions, and terms in the labs, it helps in job interviews to communicate and demonstrate your skills more confidently.
2. Participate in mock interviews and learn from experience.
3. Check out frequently asked interview questions.

Simply, it is important to take the time to prepare for the interview.

Application Strategy:

Approach the job market with a good application strategy for better results.

The application strategy includes application channels, selecting the right filters to find the job you are interested in, frequency of applications, follow-up, and more.

Finally, learn from failures:

Remember that most people land their dream job after several failed attempts. So keep a positive attitude when you fail in an interview and learn from your mistakes.

Many institutes offer structured courses in data science with various learning options. It is very important to choose a course that offers live sessions, proven mentors with industry experience and, if possible, traineeship and projects in real time.

DataMites®a leading data science institute, offers a 7-month course in Certified Data Science (CDS) accredited by the International Association for Business Analysis (IABAC®).

The Data Science Certified Course from DataMites® is combined with internship options at AI companies as well as real-time projects mentored by industry experts.

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