If you devour all things analytics, then you’ll find this blog helpful. Before you can even choose, you need to assess your fit to an analytics role. The following primer will get you started:
Understand the Analytics Landscape and Identify Your Ideal Analytics Job
So, what constitutes an analytics job? Is it the same as big data job?
The analytics landscape is fraught with over-hyped and over-used terms, so before we go further, let me briefly clarify some of the terminology. Believe it or not, “analytics” is not synonymous with “Big Data” even though these days it is often mentioned in the same breath. Let’s discuss that in a moment.
First let’s define “analytics” vs. “business intelligence” (BI). Business intelligence and analytics are actually two distinct processes that involve different tools and serve different purposes.
When a user interacts with a system (such as when you checkout groceries from your local supermarket), data is produced, collected, cleaned and stored using data solutions including Teradata, Hadoop and Oracle. Data is then accessed via reports and, increasingly, via graphical dashboards. BI includes all components of the operation, from when data is collected to when it is accessed.
Analytics, on the other hand, is the process performed on data that has been delivered by BI for the purpose of generating insights to drive decisions, actions and, eventually, revenue or other impacts. Data is converted to insights using analytics tools such as SAS, R and Excel.
Now let’s talk about Big Data. Big Data’s ever-increasing volumes, variety and velocity (known as the Three Vs) create issues of storage and visualization that make traditional business intelligence systems unstable. Big Data is thus a business intelligence issue, not an analytics issue. Our focus for this lesson, then, must exclude Big Data
What analytics jobs interest you?
Once you know you are interested in analytics, the question is, “What kind of analytics job is right for you?” Get an idea about the analytics jobs out there by typing “Analyst”, “Analytics” or “data scientist” in job forums such as LinkedIn, Icrunchdata.com or Monster. Note: If the title includes “Analyst” but the job doesn’t require analyzing data, then it is not an analytics job. For example, a “Business Process Analyst” does not have an analytics job and we will not be talking about those careers here.
Now, let’s talk about three job categories – Data Analyst, Business Analytics Professional and Predictive Analytics Professional. Each needs different analytics skillsets. For example, a business analytics professional needs strong business analytics skills along with the ability to access data through a GUI-based BI tool and analyze it in a basic analytics tool such as MS Excel. Data Analyst should be strong in Data Management Programming (SQL, Hive) and data access via BI tools, with a working knowledge of statistics and business analytics skill. A predictive Analytics professional (referred to as Analytics Professional in general) requires advanced statistics skills, A/B testing skills, business analytics skills and stat tools, with a working knowledge of data access & management.
Data Scientist, on the other hand, is used very broadly with jobs falling under all three categories. Some data scientist job descriptions seem to seek applicants strong in all three areas. I would recommend ignoring those jobs for now as it takes intense amount of learning to become that “superhuman” data scientist.
What Analytics Training you need?
What is truly needed to succeed in analytics? If you have been looking to get trained in analytics and have also been wondering how to choose, I recommend following these 3 steps to find out what you need, based your own background and where you want to go.
Identify what you want to do
What current/future role are you going for: are you/do you want to be an analyst/data scientist? Or are you a business professional, looking to leverage analytics in your day to day work flow?
Identify the skills gap you have based on what you want to do
As you can imagine, the skills needed for business professionals within Marketing, Product etc. functions to leverage data effectively is going to be somewhat different from that of a data scientist. Data scientists need deeper technical skills and skills to work effectively with business professionals. The 6 key analytics skills used by successful analyst/data scientist are:
- DTD framework: Understanding and hands-on experience of the basic “Data to Decisions” framework
- SQL skills: Ability to pull data from multiple sources and collate: experience in writing SQL queries and exposure to tools like Teradata, Oracle etc. Some understanding of Big Data tools using Hadoop is also helpful.
- Basic “applied” stat techniques a.k.a. Business Analytics: Hands-on experience with basic statistical techniques: Profiling, Correlation analysis, Trend analysis, Sizing/Estimation, Segmentation (RFM, product migration etc.). If you are in a consumer business, this list would include hands-on comfort in A/B Testing (also called Design of Experiments)
- Working effectively with business side: Ability to work effectively with stakeholders by building alignment, effective communication and influencing
- Advanced “applied” stat techniques (hands-on) a.k.a. Predictive Analytics: Hands-on comfort with advance techniques: Time Series, Predictive Analytics – Regression and Decision Tree, Segmentation (K-means clustering) and Text Analytics (optional)
- Stat Tools: Experience with one or more statistical tools like SAS, R, SPSS, Knime or others.\
On the other hand, business professionals need easy access to data through some kind of tool like Business Object, Micro strategy etc., basic analysis skills and ability to work effectively with data scientists and analysts. The 4 key analytics skills needed by business professionals are: DTD Framework, Basic “applied” stat techniques, working effectively with analysts, Advanced “applied” stat techniques (intro).
Choose the most appropriate training option
Based on skills gap you identified, then choose the most appropriate training option:
- Master’s degree in Analytics: these program is most useful for individuals with no professional experience but looking for future data scientist/analyst roles. These programs are also often taught by those in academia and may thus lack the focus on applying analytics to solve business problem.
- Semester courses at local universities: Most universities offer semester/quarterly courses from statistics and computer science department, often as part of continuing education program. These courses are most appropriate for successful data scientist and analyst who are looking to pick up incremental skills for their current analytics role
- Professional Workshop: Many consulting companies like Aryng, EMC, and online platforms like Coursera, Udemy and others offer short analytics training most appropriate for working professionals. These short courses are most appropriate for business professionals looking to leverage data to make better decisions, for professionals who are looking to make a transition to an analytics career and analyst looking to pick incremental skills.
But in the end, do your own due diligence and be sure to match the gaps you have identified with the courses you choose to take. If you are looking to transition your career to analytics or in any ways looking to apply analytics to solve business problem, make sure the programs include
- Hands-on analytics courses
- Focused on solving real business problem,
- With access to analytics expertswho have made significant impact in business using data and analytics,
- And you get to work on a real time projectwith a client so you know how to apply what you have learnt.
You are now one step closer to finding and landing that dream job!