Become a Data Science Expert: How to Nail It

How to Become a Data Science Expert? Data science is interesting. It’s also challenging, but the feeling of solving an enormous problem is immensely satisfying. Perhaps it’s this feeling that drives data science people today, aided with the fact that Data Scientists are one of the highest-paid professionals around the world.

According to Glassdoor, the average salary of a Data Scientist in the U.S. is $113,309. 

Data science is booming. Adoption of data science in the industry, for better data-driven decision making is increasing.

With this, not to mention, the demand for data science professionals is soaring. IBM predicts that the U.S. would need 2,720,000 data science professionals by 2020.

The point of all this is – businesses are deriving a lot of value from data. The amount of generated data every day is immeasurable, reaching petabytes in volume. According to an IDC report, the amount of generated every day will increase to 61% to 175 Zettabytes by 2025. For the sake of simplicity, a Zettabyte is a trillion gigabyte. 

Data Science Career Review

Businesses are coming up with all sorts of ways to extract value from this explosion of data. Companies are using this data to their advantage in various ways. 

1. Customer acquisition and retention
2. Sales growth
3. Improve customer experience
4. Drive product innovation
5. Improve organizations performance

Data Scientists and other data science professionals are behind the scenes to give shape to these projects. While they are it, Data Scientists engage in a multitude of tasks to materialize such projects. They source relevant data, clean data, analyze data, and build predictive models.

They do so single-handedly. They steer organizations in directions that are more promising and establish themselves stronger in the market.  

Steering organizations toward growth with data science   Data Scientists perform a multitude of tasks to help organizations make better decisions. There are four major tasks that fall under their purview.  

1. Data collection – Data Scientists spend the majority of their time to collect data. This data is collected from various sources and is often unstructured and messy. Data Scientists clean this data, ensure consistency in data, and furnish it for use for other people in the team.  They require extensive knowledge of data mining, R or Python programming, SQL, and database management to collect data and manage it.
   
   
2. Data Analysis – Collected data is analyzed by Data Scientists to find actionable insights that can help organizations to grow. Knowledge of descriptive and inferential statistics, probability, linear algebra, algorithms, and programming are required at this stage to perform their dutifully.
   
   
3. Data visualization – Analyzed data is communicated to stakeholderss in the form of graphs and charts to allow stakeholders to understand easily. Tools like Tableau and software packages like Matplotlib in Python and ggplots in R Studio help Data Scientists do so.
   Once visualized data is available with stakeholders, the decision is underway. 
   
4. Building predictive modes – The final step in this process is building a model, which is facilitated by the use of machine learning algorithms.

Essentially to become a data science expert, you need to master ample skills, which often doesn’t come at once, but with repeated practice.

The best option for aspiring data science professionals to master these skills is to work on miscellaneous projects. In addition to that, gaining industry validation for skills in the form of certifications is a good option.

Certifications for Data Scientists  |Data Science Career

Following professional organizations offer data science certifications that are globally-recognized and have earned a reputation in the data science industry.  

1. IBM: Offers Data Science Associate Professional Certificate, which establishes your readiness for a data science career. The certification process assesses your Python programming skills and your knowledge of database management, SQL, machine learning, statistics, data analysis, and data visualization.

• Harvard University: The University offers certification in collaboration with edX. The certification equips you with the skills needed to perform your job in data science sincerely. Taking this certification will demonstrate your R programming skills and application of inferential statistics, machine learning, and other skills to perform optimally in your role. It is an essential credential for data science beginners to put themselves out in the market.

• DASCA: It offers six data science certifications for professionals of all levels of experience including data analysts and data engineers. These certifications prove your well-roundedness for their respective roles. These are vendor-neutral certifications that establish your competency and adeptness in using data science skills to deliver business, regardless of the technology you use.
  
  Each certification with varying degrees of difficulty, which often comes with increasing responsibilities in data science roles, tests your capabilities to prove your readiness for roles. These certifications are also proof that you are proficient in using Big Data tools including Hive, Pig, Flume, and Hadoop to your advantage and deliver expected business results. 

• DELL EMC: Data Science Associate Professional is an entry-level certification offered by DELL EMC. This is a suitable certification for fresh graduates looking to enter data science. Taking this certification proves that you have working knowledge of data analysis using Python, machine learning, predictive modeling, statistics, and all that is necessary to jump-start a career in data science.
  
  

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Data science is becoming an essential part of decision making and growth for organizations. At the same time is becoming competitive too. In this scenario, for data science, professionals to standout, data science certifications serve as a medium to prove their worth.