Data Science has emerged as one of the most in-demand careers in 2021. Joining a Data scientist will give you more advantages that will help you to have a successful career.
Before we know what are the skills businesses look for in a data scientist, we should understand how data science helps businesses in achieving their objectives.
In 2020, the data that was generated per second for every person on earth was reckoned at 1.7 MB. This would mean over 2.5 quintillion bytes of data created every day.
It is crucial to know how such huge data is stored, analysed and interpreted to make sense out of it and make the decisions.
This is where Data science helps businesses in gathering, cleaning, and structuring data to analyze and make sense of it.
The need to analyze this huge amount of data also known as Big data has increased demand for data science engineers. The demand for data scientists is predicted to grow 15% by 2029, significantly faster than the 4% average for all occupations, according to the US Bureau of Labor Statistics.
Data science helps businesses in many ways. Below are a few of them
- It helps in gaining customer insights
- Protect business from fraud and increase security
- Generate financial forecasts
- Increase efficiency in the manufacturing process
- Predictive market analysis
- Making strategic business decisions
The main functions of data science are:
- Create hypothesis.
- Collect data by doing experiments.
- Evaluate the data quality.
- Cleaning and streamlining datasets.
- Prepare data for analysis by organising and structuring it.
Hence the job profile or the duties of a data scientist would be not limited to
- Developing new ways to get or capture the data required by the company and send ahead for analyzing data
- Collecting, cleaning, and processing big data.
- Understand the pattern of data.
- Adopt visualization techniques to present the data in front of the product development department and analysts and collaborate whenever required.
- Suggest solutions and approaches meet the difficulties
Do you think to perform these duties a data scientist will look at each and every piece of data?
This is where skills play a very important part. These technical skills are what every data scientist must have to help him traverse the big data and come out with conclusions. The data scientist will not see data at the very basic level, rather he or she will use certain skills to bring out the meaning in datasets. Broadly, all the skills can be classified into Mathematical, Statistical, Programming and Data analytical skills.
1. Developing Data Integrator
Developing Extract, Transform and Load (ETL) Data flow or Data Integrator or Data Pipeline consists of multiple steps like extracting data from a data source, validating, cleansing and transforming, combining data with other databases, mapping columns and data schema and finally loading the data into HADOOP, Hive, RDBMS or Cloud Data Stores.
As more businesses shift to data-driven or evidence-based strategies, skills such as data ingestion, data preparation, executing Machine Learning (ML) experiments, ML model diagnostics, and presenting outcomes are in high demand.
Multiple bottlenecks are created by effort-intensive and role/person-dependent data engineering approaches, which tend to slow down the data science experimentation process. As a result, the overall corporate transformation process is slow and ineffective. The organisations are striving to find data scientists who construct data pipelines quickly in collaborative environments using self-serve alternatives.
2. SQL and Python or R Programming
SQL stands for Structured Query Language and is widely used as a Database programming language. The most popular RDBMS packages are SQL based.
A data scientist always needs SQL to work with Structured data stored in relational databases. Hence expert knowledge of SQL is important for data scientists to query the databases, to perform data manipulation or data transformation to meet specific project requirements.
A data scientist might build a SQL query to extract certain data from a company database based on the query's requirements. Then, using their Python or R skills, they can undertake more in-depth analysis on the dataset that their SQL query retrieved and do further work using algorithms.
SQL commands also help data scientists in experimenting with the data in test environments. Hence you will find SQL as a mandatory skill in all data related jobs.
3. Data classification and Regression Analysis
Developing a classification model using various types of regression is an important part of data science.
Regression analysis is a type of predictive modelling technique that looks into the relationship between a dependent (target) and an independent (s) variable (predictor). Forecasting, time series modelling, and determining the causal effect link between variables are all done with this technique.
There are various forms of regressions. However, linear and logistic regression is most popular. The data scientist should be an expert at applying the correct form of regression. Hence knowledge of regression is indispensable.
4. Predictive Model and Explanatory Model
You can construct one of two sorts of models. One type is a predictive model, which forecasts a result based on a set of input factors. Another is an explanatory model, which is used to better understand the relationships between input and output variables rather than making predictions.
With growing needs, both models are in great demand.
A meaningful description of why and how something works, or an explanation of why something is the way it is, is an explanatory model. Explanatory models are usually used in decision science where the input has a corresponding outcome.
The predictive model is slowly gaining popularity. Statistics are used in predictive modelling to forecast outcomes. Although most of the time, the event being predicted is in the future, predictive modelling can be used to anticipate an unknown occurrence, regardless of when it occurs. The predictive model has applications in understanding customer behaviour which helps businesses to maximize their sales and profits.
5. A/B Testing or Split Testing
A/B testing is a method of comparing two versions of a variable in a controlled environment to see which performs better. One of the most well-known and extensively utilised statistical tools is A/B testing.
This is one of the most necessary skills because Data scientists are frequently required to test multiple samples of data and derive conclusions on which sample would suit the objective that they are testing it for. It usually helps data scientists in their experiments to test a smaller subset of data and derive conclusions on the entire dataset.
Using split testing, a data scientist can make a hypothesis that is yet to be proven but, if correct, would explain certain facts or phenomena.
6. Cluster Data Sets
Clustering is the process of splitting a population or set of data points into many groups so that data points in the same group are more similar than data points in other groups. To put it another way, the goal is to separate groups with similar characteristics and assign them to clusters.
For example, you want to analyze the types of customers you have in the business. It makes sense to put them into clusters to understand them better based on the specific traits that the customers might be having. It helps businesses to identify the likes of their customers and propose them suitable products which they will likely buy.
As a data scientist who deals with big data, he should be able to cluster out data points to bring in the meaningful representation of data to help the business. Another important skill that a data scientist should possess.
7. Data Visualization and Storytelling
8. Build Recommendation Engine
In today's world powered by the internet, a customer has lots of choices. A customer can be looking out for some product without actually having any idea which is the best one he can choose. This is where a recommendation engine can help.
Many businesses like Netflix, Google and Amazon are providing recommendations to their customers based on their past purchases or based on their needs and interest.
As this is a common scenario now in most businesses, a data scientist would be required to work on the past customer data and build a recommendation engine that would help businesses in showcasing the right products to their customers based on their needs and interests.
9. Natural Language Processing (NLP)
Modern businesses deal with massive amounts of data. That information might arrive in a variety of formats. Text is the most frequent format for storing such information. That data is typically very similar to the natural language we use daily.
The study of programming computers to handle and evaluate huge amounts of natural textual data is known as natural language processing (NLP). Because the text is such a simple to use and popular container for storing data, Data Scientists need to know NLP.
When faced with the problem of analysing and developing models from textual data, one must be familiar with the fundamental Data Science activities. Cleaning, formatting, parsing, analysing, visualising, and modelling text data are all part of this process.
10. Machine Learning and Artificial Intelligence
Only a small percentage of data scientists are true machine learning experts; those that are stand out. Machine learning may automate substantial elements of a data scientist's job, such as cleaning data by reducing redundancies, by employing algorithms and data-driven models to evaluate vast amounts of data.
Machine learning techniques like supervised vs. unsupervised machine learning, decision trees, and logistic regression are all familiar to the most capable data scientists.
Final Notes
While most of the skills are mandatory, others like NLP, ML and AI are optional. Advanced machine learning skills, such as natural language processing, outlier detection, and recommendation engines, will gain you extra points. The more of the above skills you possess, you will have an edge over others who do not have the skills.
Here we have covered the 10 most important skills that a hiring manager would look for from the perspective of the business. Do you have any more skills for becoming a data scientist that you wish were included on this list? Let me know what you think in the comments!
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