Data Scientist Skills – What Does It Take To Become A Data Scientist?

 Data Scientist Skills:

Data science is an umbrella term that encompasses data analytics, data mining, Artificial Intelligence, machine learning, Deep Learning and several other related disciplines. In this post, I have mentioned the necessary Data Scientist skills.

Most of the organizations have now realized the importance of data-driven decision making. Before I move forward let me list down the Data Scientist skills that will get you hired:

Statistics

• At least one programming language – R/ Python
• Data Extraction, Transformation, and Loading
• Data Wrangling and Data Exploration
• Machine Learning Algorithms
• Advanced Machine Learning (Deep Learning)
• Big Data Processing Frameworks 
• Data Visualization

Before I explain each of the above-mentioned points, let me categorize the skills.

As a Data Scientist, you’ll be responsible for jobs that span three domains of skills.

• statistical/mathematical reasoning,
• business communication/leadership, and
• programming

You’ll often be tasked with leading data science projects from end to end. Now, let me explain each Data Scientist skill one by one.

What Does It Take To Become A Data Scientist – Data Scientist Skills:

1. Statistics:

Wikipedia defines it as the study of the collection, analysis, interpretation, presentation, and organization of data. Therefore, it shouldn’t be a surprise that data scientists need to know statistics.

For example, data analysis requires descriptive statistics and probability theory, at a minimum. These concepts will help you make better business decisions from data.

2. Programming Language R/ Python:

With programming language, you can manipulate the data and apply certain algorithms to come up with some meaningful insights. Python and R are one of the most widely used languages by Data Scientists. The primary reason is the number of packages available for Numeric and Scientific computing. With the help of packages like Scikitlearn in Python and e1071, rpart etc. in R, it becomes really easy to apply Machine Learning Algorithms.

3. Data Extraction, Transformation, and Loading:

Suppose we have multiple data sources like MySQL DB, MongoDB, Google Analytics. You have to Extract data from such sources, and then transform it for storing in a proper format or structure for the purposes of querying and analysis. Finally, you have to load the data in the Data Warehouse, where you will analyze the data. So, for people from ETL (Extract Transform and Load) background Data Science can be a good career option.

4. Data Wrangling and Data Exploration:

You have data in the warehouse, but that data is pretty inconsistent. So you have to clean and unify the messy and complex data sets for easy access and analysis this is termed as Data Wrangling. Exploratory Data Analysis (EDA) is the first step in your data analysis process. Here, you make sense of the data you have and then figure out what questions you want to ask and how to frame them, as well as how best to manipulate your available data sources to get the answers you need.

You do this by taking a broad look at patterns, trends, outliers, unexpected results and so on.

5. Machine Learning And Advanced Machine Learning (Deep Learning):

Machine Learning, as the name suggests, is the process of making machines intelligent, that have the power to think, analyze and make decisions. By building precise Machine Learning models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

You should have good hands-on knowledge of various Supervised and Unsupervised algorithms.

Deep Learning has taken traditional Machine Learning approaches to a next level. It is inspired by biological Neurons (Brain Cells). The idea here is to mimic the human brain. A large network of such Artificial Neurons is used, this is known as Deep Neural Networks. Nowadays, most of the organizations ask for knowledge of Deep Learning, so don’t miss this.

Python is the most preferred language by Machine Learning experts, and TensorFlow, is one of the most famous Python libraries for creating Deep Learning Models.

6. Big Data Processing Frameworks:

A huge amount of data is required to train Machine Learning/ Deep Learning models. Earlier because of lack of data and computational power, creating precise Machine Learning/ Deep Learning models was not possible. Nowadays huge amount of data is generated at a good velocity. This data can be structured or unstructured, therefore it cannot be processed by traditional data processing systems. Such humongous data sets are termed as Big Data.

Therefore, we require frameworks like Hadoop and Spark to handle Big Data. Nowadays, most of the organizations are using Big Data analytics to gain hidden business insights. It is, therefore, a must-have skill for a Data Scientist.

7. Data Visualization:

Data Visualization is one of the most important part of data analysis. It has always been important to present the data in an understandable and visually appealing format. Data visualization is one of the skills that Data Scientists have to master in order to communicate better with the end users. There are multiple tools like Tableau, Power BI which gives you a nice intuitive interface.

Apart from all the Data Scientist skills I have mentioned above, you should also possess a data-driven problem-solving approach. This will only come with experience. 

Have a look at the below job description:

I think I have proved my point.

Conclusion:

I hope you have enjoyed reading my post on Data Scientist skills. Your journey to becoming a Data Scientist is definitely going to be pretty long. And I know, as a working professional it is very difficult to devote time to learning something new. That’s why I always recommend people to go for online training. The time is ripe to up-skill in Data Science and Big Data Analytics to take advantage of the Data Science career opportunities that come your way.

Reference : Edureka

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