The wave of big data is gathering now and has started to crest. If you want to tame this huge wave, you need to be a good surfer who can swim through the data to deliver insights.
Glassdoor just named data scientist the best job in the U.S. for the third consecutive year. Bloomberg reported an increase of 75% in the job listings for data scientists. What’s more, data analysis, data engineers and data scientists are among the top emerging jobs on LinkedIn and the list of job openings is growing every day. These incredible statistics paint a pretty compelling picture.
In short, there has never been a better time to become a data professional.
Challenges faced by large corporations and small firms alike.
Almost every organization that exists today is fueled by data in one way or another. There are petabytes of data being generated every second. And every department can and should leverage data to map out their next steps. But for a growing number of companies worldwide, the complexity and amount of unstructured data are daunting challenges.
With the advent of the Internet of Things(IoT) and accelerated applications of technology in all sectors, data positions are booming.
Financial services companies process pipelines of historical and live stream transactional data to fight fraud and provide automated online/mobile banking services. The Healthcare sector is trying to visualize and learn from genomic data to cure chronic diseases like Cancer and AIDS by embedding data science across different stages of drug research. Tech giants like Microsoft, Google, and Facebook are using people’s behavioral data to better understand and improve their sales operations.
Now that business is so data-focused, recruiters are looking for individuals who can make sense of it. Data-savvy professionals can lead to remarkable insights that will enhance the pace of growth.
Considering the magnitude of data and the lack of skilled data analysts worldwide, pursuing a career in this field will put you high in demand today and for years to come. According to LinkedIn’s report, Data Scientist roles have grown over 650% since 2012 but there are not enough skilled individuals to cater to this need.
Here are the statistics published in a McKinsey report on big data:
The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.
Prospective career paths
When scanning job sites or career portals, you will come across a diverse range of positions in analytics. Seeing this wide range of career options can be confusing and you might feel overwhelmed.
To help make sense of it all, consider that there are three main domains in data:
- Data Analysis
- Marketing Analysis
- Business Intelligence Analysis
While the data analyst role has a greater proportion of analytics and would require you to work as a specialized analyst, the marketing and business intelligence positions have a stronger component of business strategy.
Working in a strategy orientated role, you’ll be using your analytical skills to find the shortcomings and inefficiencies in the market, strategize your approach to address them to improve the business. With time, this path can lead to roles in product management.
If you’re looking to become a specialist, the data analyst position leads you to the dynamic field of data science. You can specialize in a number of skills like text analytics, speech analytics, image/video processing, predictive analytics, modeling, etc. You’ll be creating immense value here as there is a huge demand for specialists.
What makes a successful Data Analyst?
The most important trait to become a skilled data analyst is curiosity. You must have a deep desire to see through a problem, develop the ability to come up with strategic questions and to test hypotheses. Also, an affinity toward crunching numbers and attention to detail is essential.
You will also need to acquire a range of technical competencies and soft skills. Here is the blueprint of skills to become a data analysts, marketing analyst or business intelligence analyst.
1. Coding and Analytical Tools
- Python/R data science stack(numpy, pandas, matplotlib, scipy, sklearn),
- Excel Spreadsheets,
2. Data Wrangling
- Exploratory Data Analysis, Cleaning crunching, transforming, formatting
- Writing ETL scripts.
3. Data Visualization and communication
- Generating reports
- Visualization charts and plots(ggplot, matplotlib, etc)
4. Associative thinking
- Asking the right set of questions
- Business analytics
- Uncover the areas of growth in the market
5. Machine Learning
- Supervised Learning
- Unsupervised Learning
6. Experiment Design
- Modeling phenomena
- Distill testable hypotheses
That is, admittedly, an overwhelming list, especially to a beginner. But with time and the right schooling, you’ll be on your way to a rich career, armed with some of the in-demand skills on the job market for years to come.
Why not fill the gap yourself?
Success in any domain is a function of a number of factors. Opportunity. Hard work. Timing. Quality education. For a Data Analyst, this function would look something like:
Skilled Data Analyst = function(market demand, passion to implement skills and knowledge, timeliness, _________). You can fill in the rest.
OpenClassrooms is answering the call to help fill the data talent gap through comprehensive education and training. Check out their one-year, bachelor’s-level diploma program in data analysis.
Data Engineer at Elucidata. Develops algorithms for research scientists at Yale, UCLA, and MIT. Mentor at OpenClassrooms