A Guide on How to Become a Data Scientist [Role and Responsibilities]

Data science is wizardry in today’s run for business as it helps extract meaningful insights from every nook and corner and encourages efficiency in decision making. The tasks involved can appear to be backbreaking on the intellectual front but the analytical outcomes it brings out are gratifying for any organization. According to the US Bureau of Labor Statistics, the occupations surrounding data science are anticipated to hike by 31 percent between 2019 to 2029 and that is a big deal.

So what is data science and how to become a Data Scientist? Let’s explore everything in this section.

What is Data Science?

Data science refers to the study of data that utilizes programming skills, mathematics and statistics to obtain useful insights from data. Companies, on a big note, are acknowledging the significance of data science regardless of their size or industry. In order to stay under the umbrella of competition, organizations are shifting towards the development and implementation of data science techniques.

We are in the era of Artificial Intelligence and big data. You are not overestimating data if you are calling it a magic wand for companies. It helps companies dive to the depth of information and leads to enhanced decision-making by stirring raw data into crucial insights. You might be interested to learn how to build an AI app.

What Does a Data Scientist Do?

The role of data science practitioners brings computer science, mathematics and statistics to a commonplace. Data Scientists process and model data and then carry out an interpretation of the final outcomes for creating strategies for companies.

Here are the tasks many Data Scientists usually perform on a day-to-day basis:

• Develop algorithms as well as data models to predict outcomes and make data driven decisions.
• Discover patterns and trends in datasets to unfold insights.
• Use machine learning tactics in order to better the quality of data/ product offerings.
• Discuss recommendations with other teams and senior staff.
• Mobilize data tools like Python, R, or SQL in data analysis.
• Be in pace with the new findings in the data science field.

Why You Should Become a Data Scientist?

The field of data science is expanding and this is how an endless number of job opportunities are making their way in. If basking in intellectual errands and amping up decision making for a rewarding business thrills you then you already have the foremost reason to consider data science as your career destination.

Since this field is spreading its wings and paving a way for job options in ample numbers, it’s safe to say that you will have a long and bright career to a tremendous extent. Besides, by jumping your way into this option, you will have a chance to work with the business giants of the world. On top of everything, the job of Data Scientist is one of highest paid ones with an average of $100,500 per year.

Data Scientist Role and Responsibilities

The work of Data scientists involves interacting with business stakeholders to comprehend their business goals. It helps them gauge how they can use data modeling processes, make algorithms and predictive analysis to achieve those business requirements. Each industry and project is different. However, the roles and responsibilities of Data Scientists on the common ground include the following:

• Generate and handle data.
• Integrate and store data.
• Choose one or multiple potential algorithms and models.
• Carry out data inspection and exploratory data analysis.
• Use data science techniques like statistical models, statistical analysis systems, machine learning, and artificial intelligence.
• Inspect and work on outcomes for improvisation.
• Communicate the final result to stakeholders.
• Work more on them as per the feedback received by stakeholders.

Must-Have Skills to Become a Data Scientist

When you are seeking an advanced position, you need to be equipped with a compelling suite of skills in order to ace your career. When it comes to the position of Data Scientist, there are many skills you need to acquire irrespective of the roles and responsibilities for specialized Data Scientists. Let’s take a look at at the tray of skills required to become a Data Scientist:

Mathematics and Statistics

Data Science professionals have a stronghold over math and statistics. Any organization, whether or not they are data-focused, demands individuals to know and understand the ways to approach descriptive statistics which includes advanced analytics, predictive models, predictive analysis, distributors as well as statistical tests. It helps them be able to make sound recommendations and data driven decisions.

Data Analytics and Modeling

In order to accomplish an agenda in this field, Data professionals need to be skilled to perform data analytics and modeling. Any company will expect you to possess deep learning in this case. You should be able to analyze data and create models in order to gain new insights and forecast outcomes.

Machine Learning Techniques

While you may not have an expertise in the field of machine learning applications or machine learning algorithms, you will still be expected to at least have some knowledge on it. The essential elements of machine learning include logistic regression and decision trees. When you are applying for data science positions, know that potential employers will be expecting you to have this side of knowledge.

Programming

Apart from data analytics and data manipulation, data scientists need to have a strong suite of programming skills in order to ace the creation of data science applications. Most employers expect data scientists to know Python language and R and many other programming languages. In this realm, you can try touching on object-oriented programming, flow control statements and libraries, basic syntax and functions.

Data Visualization

Data visualization is a major element of becoming a professional in data science, given that you will be required to intimate key messaging effectively. You should be able to comprehend how to split complicated data infrastructure into tinier components. You should also be able to visualize data.

Communication Skills

Since data won’t be able to communicate on its own, you must know the art of data manipulation and be able to have top-notch communication skills. From intimating data science teams about the steps required to be followed to achieve a goal related to data architecture to giving a presentation, communication will prove to be a major differentiator in the final results.

Top Careers in Data Science

One of the amazing parts about the field of data science is that it unravels a variety of roles before you. So, you can choose the right job from multiple options available whether you’re a newcomer, or you’re a professional looking for a change.
Data science practitioners work in different industries and disciplines. Here are the most popular and high-paying career options in data science:

• Data Scientist
• Data Analyst
• Data Engineer
• Data Architect
• Machine Learning Engineer
• Statistician
• Business Analyst
• Database Administrator
• Data and Analytics Manager
• Researcher
• Quantitative Analyst

Best Data Science Tools

Learning how Data Science tools work will help you excel brighter in a Data Science career. Here are the best data science tools available out there:

SAS

SAS is a data science tool that is specially designed to contribute to statistical operations. It uses the base SAS programming language to perform statistical modeling. This software is used by large-scale businesses for big data analysis.

Apache Spark

Apache Spark or Spark is an analytics engine that is used the most as a Data Science tool. It is specially designed for looking after the batch processing, data preprocessing and streaming data.

BigML

BigML is yet another popular Data Science Tool for Data Scientists that offers a completely communicative and cloud-based GUI environment which can be used to process Machine Learning Algorithms. This tool brings standardized software practicing cloud computing.

Tableau

Tableau is one of the widely used data science tools. It is a data visualization software enriched with powerful graphics that help create interactive data visualizations. This tool is dedicated to industries based in the field of business intelligence.

D3.js

D3.js, a Javascript library that helps Data Scientists create interactive visualizations on their web-browser. Using various APIs of D3.js, they can take a hand from a number of functions for making dynamic visualization as well as analysis of data in the browser.

TensorFlow

TensorFlow is counted as a standard tool for practicing ML. This tool is incorporated for advanced machine learning algorithms such as deep learning.

Jupyter

Jupyter is one of the widely used data science tools that is based on Python. This open-source tool helps developers make open-source software and backs a range of languages like Python, Julia, and R.

Excel

You probably already know about this one. Excel is a powerful data science tool developed by Microsoft majorly for spreadsheet calculations. Today, this tool helps with visualization techniques, data processing, and complex calculations.

Matplotlib

It is a plotting and visualization library created specially for Python. Matplotlib is the most popular tool when it comes to generating graphs using the analyzed data. It is widely used for plotting complex graphs with the help of simple lines of code.

Ggplot2

Then there is ggplot2 – an advanced data visualization tool made for R. This tool is created in order to replace the native graphics package that comes with R. ggplot2 uses robust commands for creating illustrious visualizations.

How to Become a Data Scientist?(Step-By-Step Guide)

Here is a step-by-step guide to become a Data Scientist:

Learn the Right Data Skills and Data Science Fundamentals

If you are a newbie to data science and have no idea how it works then your first step is to create the background for working as a data scientist. The foremost part of acing the data scientist career is to encompass the breadth of knowledge in the concerned field before you jump on to the degree part. The area of knowledge, here, could cover engineering, data analysis, IT, mathematics and finance.

But everything is incomplete without the very fundamentals of data science which include SQL, Python programming language and Excel. It is best to opt for a bootcamp or a data scientist course to fathom the data science fundamentals. There, you will be able to acquire skills like how to store data, how to analyze and model data and the art of presenting data using the tools that are there in the data science toolkit such as Tableau and PowerBI.

Learn Essential Programming Languages

Data Scientists count a lot on a range of specialized tools and programs. These tools and programs are created specifically for data cleaning, analysis, as well as data modeling. The required knowledge goes beyond Excel and entails statistical packages of programming languages which include Python, R, and a query programming language like SQL.

The practice of data science also involves machine learning, a set of tools that bring in artificial intelligence for helping systems with the ability to learn as well as be accurate without the condition of being explicitly programmed. The tools incorporated for machine learning rely on the application. Data Science experts can choose from a myriad of tools like TensorFlow, h2o.ai., and Accord.Net. You might be interested to know the difference between Artificial Intelligence Vs Machine Learning Vs Data Science.

Use Data Science Projects to Develop Skills

After learning the basics of the data science tools and programming languages, you can take on projects that allow you to make use of your acquired skills. During your practice, try to cover different phases of the process starting from the research on the market or company. While industries use their own kinds of data in order to make informed decisions, it is best that you try to learn as many data types as possible.

Once you’ve learned how to use descriptive analytics to scrutinize data for patterns, you will be able to tackle statistical techniques such as predictive analytics, predictive modeling, data mining, and machine learning.

Create and Practice Visualizations

Next step to become a Data Scientist is to practice creating your own visualizations using programs like Tableau. Know that Excel is here in the picture as a useful tool for making calculations and graphs. However, developing a stunning data visualization is not all that you need to do.

You must be able to use the data visualization you create to present your discoveries to the audience as it will help chisel your communication skills. You can practice it by starting small and communicating them to a small group or your friend or even to yourself in the mirror.

Build a Portfolio

Now that you are done with your preliminary research, training, and skills, it is time to design a portfolio that showcases all your leanings and projects. Demonstrating training, qualifications, skills and projects will help your portfolio look polished and boost your chance at landing your dream job as a Data Scientist.

Github also is an easy-peasy way to display the process you have been through, your work and the outcomes. Make sure you profile your data with a stunning narrative and also, demonstrate the challenges you have faced and solved. You must keep it in mind that there is no need to put the head-to-toe of your work. Make only those pieces brim on your profile that are closely related to the Data Scientist post you are applying for.

Apply for Jobs

You will find a number of titles in the field of data science including Data Engineer and Data Analyst. Before you apply for a job, check what a company is looking for, and their goals to see if you can use your skills to help them.

Data Science Courses and Qualifications

There are more than one or two gateways to make a career in data science. But for starters, you have to enroll for a college education to give a flight to your intent. It is to be noted that 38% of the data science practitioners have a PhD while the rest have a graduate degree.

Data science degrees help you with the prerequisites of the process and analysis of a complex data set. They also teach technical information related to mathematics, analysis techniques, and so on. Many schools provide data science degrees and that is the foremost option to go with.

You must have an undergraduate/ postgraduate degree in a related discipline to work in the field of data science. You can opt for graduation in computer science, Business information systems, Mathematics and Statistics or Economics.

Furthermore, certification in data science courses can help profile your resume to an incredible extent. You can take up online data science courses that teach Linear Algebra, Statistics and Probability.

Apart from that, you must also try to touch on the following subjects:

• Information extraction
• Computer vision
• Bioinformatics
• Database management, data analysis and database designing

Apart from that, data science programs also feature a creative and analytical segment that exposes you to skills like judgment and decision making – the essential parts of the data science process.

Data Scientist Salary

A Data Scientist salary travels high and low depending on factors such as experience, region and skill set. Also, considering the average, it does not differ much from the salary of Data Analysts or Data Engineers.

While the average Data Scientist salary is $100,500 per year, the figure is tweaked by the level of expertise. For instance, an entry level Data Science practitioner gets $95,000 per year whereas an experienced Data Scientist salary goes up to $250,000 per year.

The region is also a highly notable factor contributing to the salary. Let’s take a look at the average Data Scientist salary in the USA, UK, Australia and India.