Data Analyst and Data Scientist do not differ from each other so much. Just a few sets of skills make all the difference. Transitioning from Data Analyst to Data Scientist is hard, but achievable. So many people out there have already done it, while several data analyst professionals are willing to make the transition.

What does it exactly take? Learning, practicing and problem-solving. Data Scientists work on pressing challenges to solve real business problems. Their role involves gathering data and exploring data using sophisticated statistical methods. Further, devising solutions using the same data.

A Data Analyst, on the other hand, mostly gather data from several business verticals to analyze and translate them into plain language for better decision making. What skills do you need to shift to a data science career?  Skills required for Data Scientist 

Making the transition to Data Scientist would require you to get equipped with relevant data science skills. 

1. Programming languages — A Data Analyst is mostly required to work with SQL and NoSQL to gather data. Data Scientists require good programming skills and knowledge of predominantly R, Python, or SAS languages. 
2. Data analysis – Analysts perform data analysis using Excel, while Scientists use statistical techniques to perform analysis.
3. Modeling – Analysts’ responsibilities are limited to forecasting and building dashboards, Data Scientists go beyond to build predictive models and other machine learning models. 

How to make the switch? 

1. Build relevant connection – This may sound like BS. But this is essential. Once you start connecting with Data Scientist, you will understand their work and build some understanding of their work. Eventually, it will be easier for you to work. LinkedIn is a good place to start building connections with seasoned Data Scientists.
   
   
2. Start practicing at your job – You can start practicing Data scientists work at your own organization. Take organizational data and do what real Data Scientists do. At the same time, you can use platforms like Kaggle, Github, etc. to work on data science projects.
   
   
3. Start building models – Building machine learning models is specifically meant for Data Scientists. Even though it’s a small part of their responsibilities, but it is the most significant part of their job. Analysts’ role is to find out what happened, while Data Scientists are more concerned with what could happen or what will happen, which mostly happens with modeling.
   
   
4. Get a data science certification – Building credibility for the role of Data Scientists is extremely important. Organizations are still struggling to assess a definitive approach to assess Data Scientist skills. Having a certification will build credibility among employers especially when you are transitioning to a new role with no professional experience. Some top data science certifications include DASCA’s SDS, Cloudera’s CCP Data Scientist, Dell EMC’s Data Science Associate among others.
   
   
5. Do some projects – Building a portfolio of projects is essential. You will get the hang of working with a large amount of data. These projects will help employers assess your skills more finely. The more spread out your projects are the better. Projects based on solving real-life problems will aid your chances of transitioning to the Data Scientist role faster. 

Final word 

Data Scientists are some highly in-demand professionals around the world. Their role and responsibilities are demanding across all data-related jobs. Given lucrative salaries offered to Data Scientists, data science career is rewarding and employers are now extremely diligent. So transitioning to a data science role will be challenging to stay the least, but a sincere learning approach will help you to beat it.