Top 8 skills for Data Science Managers
Data science Managers play a vital role in the digital transformation journey and add value to the organization by leveraging data for decision making. A great data scientist may not be a great Data science manager.
How are data science managers different?
Data science managers possess a larger set of skills to handle teams, projects, manage stakeholder expectations. They work with team members with diverse roles and levels in the organizations such as data engineers, data scientists, software developers, business analysts, product managers, digital marketing experts, business stakeholders, and Infrastructure teams.
In this article, I am going to cover the top 8 skills that data science managers should acquire and master.
Top 8 Must-have Skills for a Data Science Manager
1. Creative Problem Solving
2. Data manipulation and cleaning
3. Statistics and Quantitative techniques
4. Programming Language
5. Data Visualization
6. Machine Learning
7. Storytelling
8. Project Management
1. Creative Problem Solving — Understand the problem clearly. Be creative in asking the right questions to discover the business context, the priority of the problem for the business, and shortlist the problems that can be solved using machine learning/artificial intelligence. While identifying the problems and understanding the impacted variables, you can apply various tools and practices such as Plan-Do-Check-Act, Ishikawa-diagram, the Five Why technique, etc. to find the root cause — It will help you to identify and validate the data required to be collected for your analysis.
2. Data manipulation and cleaning — Data collected may have issues such as missing data, incorrect values, outliers, blank columns/rows. This will severely impact the accuracy of the analysis of data and the statistical models build using unclean data. You will need to use various tools and techniques to clean the data and treat the missing values and outliers. You should also be familiar with Feature engineering to transform raw data into meaningful features that could be used for data analysis.
3. Statistics and Quantitative techniques — A good statistician will be able to apply various statistical tests while pre-processing stage. You will need to run various statistical tests to validate the assumptions of normal distribution, conclude the results of the AB test/various hypothesis tests conducted during the Design of Experiments. You will need various skills in hypothesis tests, ANOVA, Linear Regression so that you can establish the impact of various factors in your data while analyzing the business problem.
4. Programming Language — Build your skills in a Programming language — Python is the most popular language for solving data science problems and has got a large library of packages for wide range of applications in data science and artificial intelligence. It is available on multiple operating systems — Windows, MAC, and LINUX. R is another popular language used for statistical research. Both R and Python languages have a huge number of learning resources and communities for support.
5. Data visualization — Become a visual storyteller by representing information stored in the data in the form of visual dashboards comprising of different types of charts. These charts can be line charts (trends), bar charts, scatter plots, maps, histograms, etc. You can discover hidden insights by visualizing the data in graphical form. As a data science manager, you will need to develop skills in choosing appropriate graphical elements to represent the information and interpret the graphs
6. Machine Learning — Data science managers must possess good skills in developing and interpreting the results of supervised/unsupervised machine learning models. During the data mining and predictive model building stages, you will focus on choosing the right approach to build the predictive model/solution to solve the business use case. You will need to develop skills to use various machine learning algorithms, interpret the output of the algorithms and make decisions to further iterate or optimize the performance of your models.
7. Storytelling — As human beings, we connect very well with the stories than just the individual facts. Storytelling is an important skill that data science managers must possess to convey the big picture or a complete story about the problem, the factors or underlying features that caused the problem, the solution, and the business impact. A powerful story simplifies the problem understanding and helps in faster decision-making.
8. Project Management — An essential skill for a data science manager is to become a good project manager. You need to manage various aspects of the data science project — the scope, the budget, the project and resource plan, the risks, and the assumptions in your project.
Very often, new data science projects start with a poorly defined scope or objective. As a data science, Project Manager you need to follow an iterative process of refining the scope during the define and POC stage and focus on freezing the scope. You also need to develop good skills in managing various stakeholders in the project and have an appropriate strategy to handle a variety of stakeholders in your data science project.
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