Is there a conclusive list that makes you great in a profession? In my opinion, no. Everyone is different and it is the difference that often works as an advantage.
But having said that, there are some common qualities within the diversity that helps you go in the right direction. And here are some that I discovered.
Before we go to R Programming, machine learning or statistical modeling it is important to talk about this highly overlooked but critical virtue. As I was dealing with massive data in terms of generating, recording, curating, processing and sharing, placing integrity in my work helped me produce quality output.
The lack of integrity in data scientists can be detrimental not only our own career but can affect society itself in a negative way. Some of the recent events in the industry that went mainstream are examples of that.
I never thought of myself as a curious person. I always did what I was told to do. That is the thing about data science. It makes you ask more questions because you know the answer is lying inside those numbers waiting to be read. I was pleasantly surprised with myself when I took up a side project on my own in the first month. I tried to figure out how else the company can benefit from the data we paid for.
It was a small spark of curiosity. As time went on, I nurtured it and it was a tool I used to sharpen my other skills as a data scientist.
- Using Data Science in Everyday Life
To solve business problems, I had to re-learn Statistics (using Excel this time), learn programming languages like Python and big data frameworks like Hadoop and Apache Spark. I gained a lot of new knowledge which I used in my daily life. I used data from my daily life to learn (or rather re-learn) Statistics.
By making data science a part of my daily life I not only gained practice but also profited from it. It became a part of my way of life. I did not leave my skills back in office when I came home. As I applied them more in my personal life, the more I was interested to explore further.
I created Excel reports predicting my expenses for upcoming months, planned my vacations, created marketing campaigns for my side hustle and even shopped online based on data I collected from various websites and comparing them with my interests and budget. Not ashamed to say I had reports for my grocery shopping either.
Whether you are working on the technical side or if you are more on the business side of the shop at the end of the day we are trying to tell a story. It is not a skill that everyone possesses, especially not me, a total introvert.
Enrolling in my local Toastmasters group was a great asset for me. It improved my presentation skills to the nth degree. They helped beyond measure to get my audience interaction right, on how made my presentations, sticking to the time limit, reduced the time I used faltered, grammatical mistakes etc.
Even though there are many online courses on communication, and it may work for many people, working on speeches week after week helps in spades. If there are no Toastmasters near you, work with your colleagues or friends. Or better still, just step up and take up responsibility for presentations voluntarily at work. I know it is easier said than done but watch yourself do wonders within two weeks.
- Team Spirit
I am not a sports person like most. I cannot catch a ball to save my life, so it does not come naturally to me. When you are working as a team always keep the eye on the goal and understand you are all here to solve the problem.
Since we work at highly diverse people, it is more important than ever to harness empathy and fair-mindedness. Leaving the ego at the door and a sense of fairness were the two factors that helped me work with diverse teams and leading them.
- Studying Business Cases and Corporate Finance
When I started working, I like everyone else in the building started noticing the issues that are stopping the organization reach its goals. There were operational, financial, cultural and even political problems. But how do I get over so many issues? I wanted to help but did not know how.
That is when my boss suggested I study business cases on strategies. I studied strategies of corporations that became successful and failures gave me a lot of insights to apply to my project. They helped me in understanding the customer better and delivering them better products.
When he also suggested I spend some time learning corporate finance, I was baffled. I am not a manager. Why should I learn to decipher an annual report? Why should I understand what EBIDTA is? When I gave it a shot, I found it hard and figuring it out did not come naturally to me. But once I got the hang of it I found it incredibly interesting.
Combining my studies on strategies along with finance added an amazing boost to my business acumen. It helped me understand what exactly my client needed and pitch for projects accordingly to bring revenue and loyal customers for my organization. It helped me become more solution-oriented than just pointing out faults on why I didn’t meet my goals.
- Mathematics and Statistics
I always got bad grades in Math. Always. And I am a data analyst. Who knew this day would come?
As days passed by after I joined, to answer the questions that emerged from the business and the data, I realized one way or the other I absolutely need to be good at it to solve the problems effectively. I researched online and asked a few of my colleagues about the very basics I needed to re-learn. The following are some of the most important Statistics and Mathematics topics. According to your requirement, you can add or remove topics to it if you need to re-learn some stuff as well.
Data Summaries And Descriptive Statistics
The Central Tendencies
Geometric Mean and it’s Applications
Median and it’s Applications
Mode – it’s advantages and usage
Range – Advantage and disadvantage
Average absolute difference
Variance and Standard Deviation
Coefficient of Variance and Z Statistics
Continuous vs Discrete
Central Limit Theorem
Type 1, Type 2 and Power
Z – Test
Student’s T Test
F – Test
1 Tailed and 2 Tailed Tests
Monotony vs Linearity
Advantages and Disadvantages of Parametric Method
Mann Whitney U Test
Kruskal Wallis H Test
One Sample Run Test
Spearman Rank Correlation
Goodness of Fit
Simple Linear Regression
Some important Mathematics topics.
Real and Complex Numbers
Linear and Quadratic Equations
Permutations and Combinations
- Programming Languages
Before I began any programming language, I used Excel to learn all the concepts and get the basics right. Excel is a powerful tool and it is used for a wide variety of analysis and visualizations. However, if you want to learn machine learning, use huge volumes and various formats of data, learning Python or R is required. Python is much easier to learn than R for beginners. There is a wide range of resources on the Internet for both the languages.
- Big Data
I am sure I do not have to explain the importance of big data and how it has been changing business models all over the globe the past decade. I am working on both Hadoop framework and Apache Spark. Even though there are loads of information available on the Internet, I would suggest getting hands-on experience by learning from people who work on Hadoop and getting access to a system to work on. That way you can really put your skills to test. Again, there are loads of information on the Internet and forums for you to work with.
Like I had mentioned above, at the end of the day, we are telling stories. To do so effectively, we need visualizations. There are several tools like Tableau, Excel, ggplot, Matplottlib etc. and many more to help you with it. I found Tableau to be extremely useful and intuitive to understand without learning any code behind it.
- Be Yourself and Do the Right Thing
These are the steps I followed to help me become helped me be passionate about my profession, progress steadfastly and add zest to an at an otherwise doomed professional life. I have a long long way to go and I hope my story will help you if you are feeling under-confident or feel that you lack the skills. You can do it. Trust me.
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