So, do you want to become a successful Data Scientist and also want to earn more? Well you can. To this end, we shall explore some Data Scientist skills, you need for a successful Data Scientist career.
- 1 Improve Data Scientist Skills by Following World Leading ultimate Data Scientists:
- 2 Basics to know before starting a career in Data Science:
- 3 What are the top 10 Skills that required to be Successful Data Scientist:
- 4 Final thoughts:
And you know? A report by The Economic Times dated February 28, 2019, says that almost 97,000 jobs in analytics and data science are still vacant in India. Why are those job positions not yet filled? Answer is simple- due to lack of skilled workers. Therefore, if you possess the data scientist skills, then there would be a great scope for you in India.
So, If you are already a Data Scientist, I would first suggest that you follow some leading Data Scientists who are on twitter. As this helps you to get personal inspiration from them.
Improve Data Scientist Skills by Following World Leading ultimate Data Scientists:
By following these role models on a daily basis provides you an inspiration, a motivation to find bigger purpose in life and to achieve it.
1. Dean Abbott
Dean Abbott, who is the co-founder as well as chief data scientist at SmarterHQ, and the founder and president of Abbott Analytics. He is known to be the co-author of the IBM SPSS Modeler Cookbook, also the author of Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst. Follow his blog at http://abbottanalytics.blogspot.com.
Kenneth Cukier is known as the data editor for The Economist. He is also considered as the co-author of the book Big Data: A Revolution That Will Transform How We Live, Work, and Think, and is regarded as a popular speaker. Get inspired by watching him giving a fascinating TED talk on “Big data is better data.”
3. John Elder
John Elder is known to be the founder of data mining consultancy Elder Research, Inc.It is also notable that John is the author of several books, including the Data Mining Applications, Handbook of Statistical Analysis and Practical Text Mining, and Ensemble Methods in Data Mining. He is also an adjunct professor at the University of Virginia. He is known to be a frequent keynote speaker. You can follow him by watching many of his presentations on YouTube.
4. Bernard Marr
Bernard Marr, is the CEO and founder of the Advanced Performance Institute. He is also known to be the most familiar contributor to the World Economic Forum. It is notable that Bernard is recognized by LinkedIn as one of the world’s top 50 business influencers. Also, he is a frequent keynote speaker. Bernard is the author of many articles and books, that includes Big Data: Using SMART Big Data, Analytics and Metrics to Make Better Decisions to increase Performance.
5. Hilary Mason,
Hilary Mason, is the founder of Fast Forward Labs, and also served as chief scientist at Bitly, Inc. She is the co-founder of HackNY. Hilary enjoys speaking and she is a member of NYCResistor. It is also said that Mason is a popular influencer on LinkedIn. So follow her as your inspiration, you can find many of her presentations on YouTube.
Basics to know before starting a career in Data Science:
As a data scientist, you do research and analyze data to help companies grow by depicting trends, growth, and business insights from an overwhelming data.
Therefore, as a Data scientist, you require a diverse set of skills. As it is considered to be an interdisciplinary field that draws on aspects of science, math, computer science, business and communication.
So, before knowing the skills for a Successful data scientist. You must have the eligibility to start a career in this sought – after field.
Hence, to start your career in Data Science, you must have a strong educational background with depth of knowledge about the subject. Therefore, you should have at least of 88% in a Master’s degree and 46% in PhDs .
To become a data scientist, you must have at least a Bachelor’s degree in Computer Science, Physical Sciences,Social Sciences, and Statistics. The most common fields of study include Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%).
Therefore, having a degree in any of these courses will help you provide the skills that you required as a Data scientist for processing and analyzing big data.
Once completing your degree with higher percentage is alone not enough. The fact is, most data scientists have done their Master’s degree or Ph.D. So to stand out from the crowd, and also to get the most in-demand jobs. You must possess data science skills by undertaking online training or best Data science courses to learn a special skill like how to use Hadoop or Big Data querying.
If you are interested to start your career as a Data Scientist, you can enroll for a master’s degree program in the field of Data science, Mathematics, Astrophysics or any other related field. Thus skills you have gained during your degree programme will help you to easily transition to data science.
Despite of classroom learning, you can also practice in your own of what you’ve learned in the classroom by building an app. Therefore, starting a blog or exploring data analysis will enable you to learn more.
What are the top 10 Skills that required to be Successful Data Scientist:
As a successful Data Scientist, you’ll be responsible for work that spans between these three domains of skills.
- statistical/mathematical reasoning.
- business communication/leadership
#1 Programming Skills:
This is the reason why you must have a degree in computer science for a data scientist. No matter what type of company or job position you’re interviewing for. Every hiring manager likely going to be expected to know how you’ll be using the tools of the trade.
This means a statistical programming language, like R or Python, and a database querying language like SQL , that every data scientist should know.
For data science, R programming is generally preferred. Moreover, R is specifically designed in a way for data science needs. Hence, you must know how to use R to solve every problem that you may encounter in data science.
Another important and common programming language, a data scientist to be known is python. Therefore, along with python, you must also know languages like Java, Perl, or C/C++. It is said that because of the versatility of Python, it is almost involved in every data science process.
#2 Hadoop Platform
It is reported that, Hadoop is the second most important skill for a data scientist with 49% rating.
As Hadoop is not a requirement, but it is used in many data science cases. Therefore, having experience with Hive or Pig will be considered to be a strong selling point. Additionally, having familiarity with cloud tools such as Amazon S3 can also considered to be beneficial.
Thus having experience on Hadoop, you can use it for data exploration, data filtration, data sampling and summarization.
As statistics is important at all company types. But especially when it comes to data-driven companies, where stakeholders will merely depend on your help to make decisions and design / evaluate experiments.
Therefore, having a good understanding of statistics is an important skill set for a data scientist. Here, you should be familiar with statistical tests, distributions, maximum likelihood estimators, etc.
#4 Apache Spark:
One of the biggest big data technology that now wide spreading everywhere is Apache Spark. As it is considered as the big data computation framework just like Hadoop. But the only difference between Spark and Hadoop is that Spark is faster than Hadoop. This is because Hadoop reads and writes to disk, which makes it slower, but with Spark, it caches its computations in memory.
Thus, Apache Spark is specifically designed in a way for data science to help run its complicated algorithm faster. As it helps data scientists in disseminating the data processing when they are supposed to deal with a big sea of data. Therefore having hands on experience with Apache Spark helps in saving your time.
#5 Machine Learning and Artificial Intelligence:
As I mentioned above, there are almost 97,000 jobs in analytics and data science are still vacant in India. This is due to lack of skills, mentioning that most of the data scientists are not proficient in machine learning areas and other techniques.
This includes neural networks, reinforcement learning, adversarial learning, etc. So, if you want to stand out from other data scientists and to fill the vacant job positions. You need to know some specific Machine learning techniques such as decision trees, supervised machine learning, logistic regression, etc. If you learn these skills, it helps you to solve variety of data science problems that are based on predictions of major organizational outcomes.
Also as a good data scientist, you must have advanced machine learning skills such as Supervised machine learning, Unsupervised machine learning, Time series, Outlier detection, Natural language processing, Computer vision, Survival analysis, Recommendation engines, Reinforcement learning, and Adversarial learning.
#6 Data Visualization:
We all know that a business frequently produces a vast amount of data. So instead of showcasing the raw data, expressing them as graphs and charts will be easy to comprehend.
Therefore, as a data scientist, you must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. As these tools helps you to convert complex results from your projects to a format that will be easy to comprehend.
#7 Unstructured data:
Unstructured data is known to be undefined content that does not fit into database tables. Examples include blog posts, audio videos, social media posts, customer reviews, video feeds, etc.
Therefore, as a data scientist, you must have the competence to understand and manipulate unstructured data from different platforms.
#8 Multivariable Calculus and Linear Algebra:
You may wonder why I need to understand these concepts to start a data science career. But having knowledge of Multivariable calculus and Linear Algebra are most important for companies where the product is defined by the data. Here small improvements in predictive performance or algorithm optimization can also lead to huge wins for the company.
Therefore, it became worth it for a data science team to build out their own implementations in house.
#9 Software Engineering:
If you’re interviewing at a smaller scale company and are one of the first data science hires, then it is important to have a strong background in software engineering.
This is because, you’ll be responsible in handling a lot of data logging, and most potential in the development of data-driven products.
#10 Data Wrangling:
Too often , as a data scientist, you are supposed to work with messy and imperfect data, which seems to be difficult to work. Some examples of data imperfections include missing values, inconsistent string formatting.
Thus, it is important for every data scientist to have this skill to work with messy data.
You may think, it was a long and winding list.But want to become a successful Data scientist and loves to earn more. You know in practice it all becomes really simple as well as exciting. Additionally, you can also undertake data science training courses from a reputed training institute.
The above Data Scientist skills, when put into sincere practice, will take you to unthinkable career heights. Best Wishes!