Data analysis can be simply defined as inspecting, segregating and analyzing the data, extracting useful insight and making a comprehensive report using statistical techniques. A huge amount of data is being generated from the internet. Companies are trying to extract information from this data to optimize their performance. Hence, in today’s job market, data analytics jobs are selling like hotcakes! A research done by a tech analyst firm Foote Partners reveals that the sudden boom in the field of Internet of things (IoT) has created a heavy demand for advanced data analytics jobs. Whether it is the health industry, retail sector or even the quality control systems of the financial world, data analysis is playing an important role. As per IBM predictions, by 2020, it is expected to see a demand spike of 28%, which approximately comes to 2.7 million new openings in this field. Around 80% of companies are paying high salaries to hire expert analysts and form an efficient data analysis team to create better business strategies in the coming years. So, one can reckon that the data analytics salary would be quite alluring! Read this blog to get an idea about what constitutes a data analytics job.

Ways to Grab Data Analytics Jobs

With the burgeoning flow of data and 360-degree transformation in people’s interests and requirements, the need for segregating quality data has become crucial for organizations. To predict the expected direction of the flow of business, organizations are continuously trying to track existing trends and read customers’ minds. Data analysts and scientists are helping these organizations to acquire the required data. To acquire a data analytics job, the most important requirement is certification and training. Candidates should possess the ability to analyze and create valuable information. So, let’s go step by step:

  1. Hone Your Technical Skills

For the role of data analyst or data scientist, one should sharpen their coding skills. Many open-source tools with various functionalities have emerged in the market, which is especially capable of handling huge amounts of data. Let’s take a more in-depth look at the technical skills that are required.

A.I) Python 

   It is one of the most commonly used, open-source programming languages. Since it is object-oriented and versatile, most of the coders use it along with JAVA, Perl and C/C++. As per this article on why data scientists love Python, many as 48% of the data analysts prefer Python over other programming languages. It can take various forms of data and import SQL tables efficiently. It has an active community with volunteers, who are creating efficient libraries and tools continuously. If you have queries, they are just one click away. Also, Python is easy to learn. It is more scalable than other languages, such as MATLAB and Stata, as it is faster and more flexible. Python also provides an ever-increasing array of libraries like Pandas, StatsModels, NumPy, SciPy and Scikit-Learn that go well with data analytics jobs. Matplotlib eases the use of graphics and visualization with the help of graphs and charts.

A.II) R Programming

       It is an open-source programming tool used for heavy statistical computing, data analytics, predictive modeling, graphical visualization, and scientific research. R programming is used for mammoth computing needs and creating software and applications that can accurately carry on statistical analysis. Just like Python, R also boasts of a huge active community, comprising competent statisticians and data scientists from all over the world. 

A.III) SQL (Structured Query Language)

Data analysis is all about a large amount of data stored in databases. SQL helps one to navigate through huge relational databases. It is used to read/write/update/retrieve data from a database. Without knowing SQL, working on data analysis is impossible. Just like Python, SQL is fairly easy to learn. However, it is a non-procedural language, which is incapable of writing a complete application. Yet, without it, it is impossible to glide through the sea of information saved in the databases.

A.IV) Hadoop

After browsing through the databases, a data analyst retrieves relevant data. Then, using statistical analysis presents a comprehensive report to the client. But the challenge is, to process and store such volumes of data, a huge platform is required. The humongous amount of data produced by modern industries has caused a monumental shift in the way data is being stored. Hadoop plays an important role in quickly and flawlessly processing this data and storing it. The four core modules included in the basic framework are:

  • Hadoop common
  • Hadoop distributed file system
  • Hadoop YARN
  • Hadoop map-reduce

“Spark” is another platform that interactively allows the processing of real-time information. Other than these skills, a person who is looking for a data analytics job should also be aware of other things like graphical visualization and Apache. Additional knowledge of supervised/unsupervised machine learning, natural language processing (NLP), outlier detection, computer vision and AI make you eligible for a higher level in the data analytics salary spectrum. Familiarity with visualization tools like ggplot, d3.js, matplotlib and tableau will aid in converting complex data into a comprehensible form.

2. Learn Necessary Soft Skills

Other skills required for getting a data analytics job are: 

  • Good communication skills 
  • Creativity 
  • Teamwork 
  • Analytical skills 
  • Detailed knowledge about business

3. Certification Courses

The above skills can be acquired through certification courses. To choose the right area of expertise, read this article. To be a successful data analyst/ data scientist, a formal course is highly recommended. Springboard India offers an online course with a duration of six months for the same. On completion of this course, you will be ready to work on practical projects with the following skills:

  • Python Data Science Stack 
  • Data Wrangling 
  • Exploratory Data Analysis
  • Data Visualisation 
  • Statistical Inference 
  • Data Science at Scale 
  • Machine Learning Specialization

The course also offers hands-on experience with 15 case studies and assignments. With this interactive online course, you get 1:1 mentoring session with industry experts and get to work with AI experts.

We are living in a world of expanding the firmament of data. With an increase in the number of internet-connected devices, the stack of structured and unstructured data is growing rapidly. To manage this data and make optimum use of the information available, companies are searching for data analytics and data scientists.  According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020. With a 1:1 mentoring-led and project-driven Data Analyst career track program, a portfolio of real-world projects, a personal career coach and job-guarantee, Springboard has it all you need to become a successful data analyst.