So, you want to be a data scientist, but don’t know how? Are you unsure of what skills are needed for a data scientist job? Do you think it’s rocket science? Are you also wondering if there are any shortcuts (read courses) to becoming a data scientist? Do you want to move from a software developer role to a data scientist role?
If yes, then read on.
More than 2.5 quintillion bytes of data are created every single day! Big data has become the cornerstone of every business, government, and non-profit organizations that need to sort and interpret this data.
Okay, there’s all this raw data but how do we make sense of it? This is where Data Science comes into play; it is the multidisciplinary study of data involving science, technology, algorithms, and system analyses to extract meaningful information.
Here are some examples of how data science is applied to your daily life: Netflix mines user movie viewing patterns to understand, suggest, and even make original movies that target users’ interests. Procter and Gamble uses time series models to predict future customer demands, which can help in planning for better production. Amazon detects users’ buying patterns to provide shopping recommendations.
This magic is driven by humans, namely, Data Scientists, the demigods of data, who can unravel its mysteries. Data scientist jobs are therefore hot and trending now because every business needs a data scientist at one level or the other. According to Harvard University, “data scientist is the sexiest job of the 21st century.”
So, how do you go about becoming a data scientist? Let’s look at some basics.
What do Data Scientists do?
Data scientists can extract information from raw data, refine it, analyse it, provide business insights, predict future events, and generate reports. Data scientists understand business problems, analyze and provide hypotheses, clean or trim data, and develop statistical models to provide solutions. Data scientists are detectives ferreting out the truth.
All this is done using different techniques such as algorithms, data visualization, and statistical modeling that detect patterns and provide solutions. Data science, therefore, pans across a variety of disciplines such as science, technology, statistics, mathematics, etc.
Understanding the job roles of a data scientist
Like in any other job, there is a hierarchy of data scientist job roles: entry-level, intermediate level, senior level, and the top-level (director) jobs. Depending on your current position, you can decide what skills you would need to perform at various levels. Alternatively, a data science professional can also be known by other names such as data engineer, data analyst, data visualization expert, machine learning expert, business analyst and so on. Your skillset must match the job role accordingly.
So, are you ready to take that leap of faith?
Okay, so you love what you have read so far, are ready to ride the data wave, and land that dream job! But, how? Is it easy to take that leap from your current profession? What skills would you require?
Data science is a complex field since it involves a variety of disciplines, as explained earlier. As a result, learning data science can be daunting. More so when you are just starting your career. You’ll be faced with a multitude of questions like what are the education prerequisites? Which tool to learn? What course to take? Do I need coding skills? Should I know statistics? And so on.
Check out these quick tips on selecting the right course and landing that dream job!
First things first
Becoming a data scientist does require some basic educational background. A college education (bachelor’s degree) is a must, preferably in any one of the following areas: computer science, mathematics, statistics, IT, or any other related discipline. As many as 73% of the data science professionals working in the field are graduates and 38% are doctorates.
That said, it need not be a major hurdle as there are many data science courses available for you to bridge the knowledge gap. A good data science course is as good as a college degree.
What courses would help bridge the gap?
Now that you know a bit about data scientist roles, make sure you understand what is needed for the job and then check out relevant courses.
When it comes to courses in data science, you’re spoilt for choice. There are career tracks in data science, full-time classroom courses, as well as online ones. Many open courses such as MOOCs offer data science courses for free. However, check out the course curriculum before deciding to enroll. Alternatively, there are many accreditations and certifications (for example, Microsoft Certified Azure Data Scientist Associate, SAS Certified Data Scientist) that can help you fast track your data science career.
How does a data science course help you?
A good data science course is structured and flexible enough to fit into your current routine. Courses can offer PG Diploma or Certificates in data science and can be either full-time or part-time. Many courses are online and are tailored to suit your pace. The duration of these courses can range from 6 months to 1 year.
The right data science course offers a comprehensive and a complete range of data science tools and concepts while setting you up for success in your data science career:
- Programming languages like Python, R, or SAS. Python is the most important language for an aspiring data scientist and is preferred over Java or C++, as it is more dynamic and structured. This is also a robust tool to handle structured data.
- RDBMS tools: SQL skills
- Spreadsheets: MS Excel or others
- Machine learning: Concepts and tools such as Google ML kit, OpenNN, or others
- Hadoop or Big data tools: Hands-on experience on big datasets
- Data visualization: Concepts and visualization libraries or tools such as Tableau, D3.js, etc
- Logical and analytical skills
- Data science algorithms and statistics, tools such as Spark, Scala, etc
What courses can I take?
Many data science courses are available in the market. Check out Springboard’s Data Science Career Track, a comprehensive and unique course covering key aspects of data science such as Data wrangling, Statistical inference, and machine learning. They also offer career guidance and mentoring, which is extremely useful when you are just starting your career or jumping ship.