Learning the Secret Sauce to Become A Successful Data Scientist
By: Anamika Singh
Tags: Data Scientist, Big Data, Data Science, Careers, Technology

The secret sauce to become a successful data scientist does not have to be hard. Read this…

With the value of data science becoming more obvious across industries, the need for data scientists is becoming pronounced. At this time, transitioning to data scientist will be a savvy move to add value for business progress, gain higher returns, and achieve career success. Let’s unravel the secret sauce to become a successful data scientist here.

“If you wanna do data science, learn how it is a technical, cultural, economic, and social discipline that has the ability to consolidate and rearrange societal power structures”, says Hugo Bowne-Anderson.

As data scientists help businesses navigate the world of data, connect new and significant patterns in business trends, make innovative solutions, and secure business processes, an investment in the right knowledge alone can pay the best interest.

Before getting into the details, take a look at the roles and responsibilities of a data scientist.

Roles and responsibilities of a data scientist

It’s all about empowering the data ecosystem, helping organizations to use their data to solve business problems and challenges.

With the humongous data collected by the organization, data scientists provide solutions to business-related problems. Using mathematics, statistics, and programming skills, data scientists organize the data and predict the output with data. They are mainly involved in:

Identifying business problems and predicting outcomes

Cleaning the structured and unstructured data

Cleaning and validating data to ensure accuracy and completeness

Analyzing the data to identifying patterns and trends.

Devising and applying new models and algorithms

Finding opportunities and developing data-driven solutions

If you want to be a successful data scientist professional, then it is crucial to understand the entire data science concept, tools and techniques, and skills for this challenging role.

Essential skills of a data scientist

The essential skills include technical skills and non-technical skills. But then there is the understanding of the business needs. Plus, data scientists cannot work alone, but works collaboratively and bring together the skills of a team. They should be creative and also ready to work outside their comfort zone. Professionals who can think like an entrepreneur will have the most impact.

The best data scientists have the vantage point to see the challenges and possibilities across industries and types of organizations. They are able to convert the organization’s goals into questions and answer them through algorithms and data.

To be an effective data scientist, you should possess the following technical and non-technical capabilities.

Technical skills

This includes core skills of data science.

💻1) Programming skills:

As a data scientist, you should have strong knowledge of programming languages such as Python, C, C++, Java, Perl, and SQL. Of all these, Python is the most popular language used in all data science job roles.

Get details about programming languages: Top 6 Programming Languages for Data Science in 2021

💻2) Analytical skills:

Hadoop, Spark, and SAS are the most widely used data analytics tools. A data science certification in any of these tools would help you improve your knowledge and skills in using them for business benefit.

Here’s a list of widely used Hadoop analytics tools that triumphed the charts: 7 Best Big Data Hadoop Analytics Tools in 2021

💻3) Working with structured and unstructured data:

Data scientists generally obtain raw data from various sources and channels including websites, IoT devices, social media channels, mobile, etc. Therefore, it is recommended to understand how to use social media and marketing strategies, to provide more effective real-world solutions to the team.

💻4) Machine learning skills:

In today’s world, data scientists may have to work with machine learning engineers for several projects. A data scientists’ perspective is essential for product development to increase commercial value. Becoming intuitive for automated solutions offered by machine learning algorithms is crucial.

Some of the technical skills and programs to master in machine learning are briefed below.

Applied mathematics : It is recommended to be familiar with linear algebra, probability, statistics, multivariate calculus, and, distributions.

: It is recommended to be familiar with linear algebra, probability, statistics, multivariate calculus, and, distributions. Computer science fundamentals : Possess knowledge of different computer science concepts such as data structures, algorithms, space, and time complexity.

: Possess knowledge of different computer science concepts such as data structures, algorithms, space, and time complexity. ML algorithms : It is good to have a sound knowledge about K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, etc.

: It is good to have a sound knowledge about K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, etc. Data modeling and evaluation : It is crucial to get skilled in data modeling and evaluation as a data scientist. You should be able to understand the underlying structure of the data and evaluate the data using algorithms effectively.

: It is crucial to get skilled in data modeling and evaluation as a data scientist. You should be able to understand the underlying structure of the data and evaluate the data using algorithms effectively. Neural networks : This is one of the skills which you cannot ignore at all. Be familiar with types of neural networks such as Feedforward neural networks, recurrent neural networks, convolutional neural networks, modular neural networks, etc.

: This is one of the skills which you cannot ignore at all. Be familiar with types of neural networks such as Feedforward neural networks, recurrent neural networks, convolutional neural networks, modular neural networks, etc. Natural language processing : Be familiar with one or more libraries such as the natural language toolkit for creating applications related to NLP.

: Be familiar with one or more libraries such as the natural language toolkit for creating applications related to NLP. Programs and tools: It is crucial to have a solid understanding of the programs and tools such as TensorFlow, Spark, Hadoop, Apache Kafka, Weka, MATLAB, Google Cloud ML Engine, Amazon Machine Learning, PyTorch, and Jupyter Notebook.

To know more about machine learning skills, read this: Why data scientists should learn machine learning.

💻5) Data visualization:

As a data scientist, you must simplify your project and make it consumable for stakeholders, prospective clients, non-technical staff, marketing team, and business leaders. Data visualization helps you to present the project in a simple visual format that everyone can understand.

To know more about data visualization tools, read this: A Complete Overview of the Best Data Visualization Tools

Non-technical skills

This includes personal and communication skills.

🏃🏻1) Business acumen:

To channelize your technical skills, it is essential to possess a strong aptitude for business. it enables you to identify business problems, predict future challenges, and explore new opportunities.

Learn more about data scientist’s role in business success: How Data Science Adds Value to your Business

🏃🏻2) Communication skills:

Communication skills are important for data scientists as they interact with both technical and non-technical teams, clients, and leaders. You should be able to communicate the analysis and findings in a way that the rest of the team understands. To communicate the information in a simpler chunk is of utmost importance.

🏃🏻3) Data intuition:

Success can be as easy as picking a low-hanging fruit many times. A successful data scientist has a keen sense of data patterns and knows how to find insights. It is essential to not miss an opportunity by exploring all the potential benefits that are still there in our grasp. Intuition leads to building an innovative culture organization-wide.

A zeal for knowledge is important as data science is still evolving. Natural curiosity and structured thinking can make you better over time and improve your chances of business success. Curiosity also helps you to become more accurate with data findings.

🏃🏻5) Business sense:

Business sense helps data scientists to interpret data for descriptive, predictive, and prescriptive analytics. You need not be an expert business analyst, but you should be able to question, find and collect data that might answer the question, analyze the data, and interpret findings.

So, now you have a brief idea about the essential skills, roles, and responsibilities of a data scientist. It’s easy to gain this knowledge and ideas by learning through proper channels. The real challenge is the thought process and its realization. Of course, this comes with experience. Working on several projects from different industrial verticals will give you the best thought process and it keeps evolving as years add on to your experience. Still, here is a sneak-peek of these thoughts, that will align with your goals.

How do successful data scientists think about data?

To design, develop, and deploy the right solutions for business, the way you think and conceptualize solutions also influences a lot.

“someone with a deeper understanding and intuition for what they are doing is a true data science whiz, and will likely have a successful career in this field.” -Lee Barnes, Head of Paytronix Systems

There are several models representing thinking patterns and as a data scientist, following them as applicable would help you to model data and communicate with your decision-makers effectively. Two of them are briefed here as a reference.

1. Design thinking mindset

Design thinking is nothing but a structured approach taken to solve problems. It comprises qualitative activities supporting the generation of human-centered design solutions. Though the activities vary from project to project, the core theme revolves around — empathize, define, ideate, prototype, and test.

📌 Empathize

Conduct ethnographic interviews to gain a deep understanding of the user journey and the pain points. Open-ended questions throw light on conclusions that we would never have arrived at through deductive reasoning alone.

📌 Define

The systematic acquisition of data will help you to form and test hypotheses against quantitative evidence. It serves as the starting point for innovation while addressing users’ needs in a unique way.

📌 Ideate

Here, there is every chance for process automation. You can design and train a behavioral model that can measure how and to what extent the hypothesized solutions can address the pain point and predict its influence on user behavior.

📌Prototype

Involve visual designers, interaction designers, and strategists to sketch out product features, build user journeys, develop business models, and product roadmaps. Of course, this might undergo extensive iterations to address the pain points uncovered during research, maximize product uptake and bring higher returns or revenue to the organization.

📌 Test

Testing uncovers the mistakes or opportunities you did not realize. There could be a pain point or user behavior that was not identified earlier but became apparent when users started to interact. This helps to change or refine the product features, if and as needed.

2. Agent-based thinking

It is a simulation modeling technique and conceptually deep that offers solutions to real-world social or business problems. The agent-based modeling mindset refers to describing a system from its constituent unit’s perspective. It is similar to microscopic modeling.

The thinking is here is more individual-centric like the physical/mental state of an individual, the individual’s interaction with the environment and other individuals. This individual-centric thinking is applied to physical assets, individual consumers making purchase decisions, organizations making markets efficient, and even the government.

From a business context, it can be applied for traffic, customer flow management, stock market, operational risk, organizational design, innovation of products, and adoption dynamics.

The model illustrated here describes a multi-scale agent-based model to study the impact of cohorting strategies on Covid-19 dynamics in metropolitan cities. The studies provide a quantitative trade-off on cohort size and its impact on disease dynamics. It shows that cohorts help to reduce disease transmission without significantly impacting ridership or social activity.

In addition, there are other models such as behavioral thinking, systems thinking, and forest thinking. To summarize, data scientists are deep thinkers with an intense intellectual curiosity to discover new things. Above all this, taking the right approach is also essential.

Let’s see how…

How does a successful data scientist take the right decision?

According to data scientists, data science can be used in different ways depending on the industry, business, and its goals.

Jonathan Nolis, a data science leader says that the ability to make good PowerPoint slides is important than using the most sophisticated deep learning models. The concept here is that the skills we know today will change tomorrow. In the open-source ecosystem of tools and commercial tools, there is ever-increasing automation, and therefore, the skills have a relatively short timescale.

Further Jonathan Nolis divides data science into three components

Business intelligence : Taking the company’s data in front of the right people

: Taking the company’s data in front of the right people Decision science : Using the data to make a decision in the company

: Using the data to make a decision in the company Machine learning: Putting data science models into production continuously

To be a successful data scientist, one should focus on critical thinking, quantitative and domain-specific skills apart from techniques.

Let’s understand the right approach a data scientist can take to excel in the career:

📍1) Practical approach

Data science is more than knowing the tool. It is about applying the experience to real-world problems and having that intuition to get the results. It is all about how to get results and when to trust results.

📍2) Consider the vertical aspect of a task

Data scientists today build practical modeling and exercises on KNIME. It is necessary to work on real-world, look-alike database problems. Of course, you can break standard analysis methods and drive new solutions. Understand 360-degree aspects of a data science problem.

📍3) Understand models and algorithms

As a data scientist, you must be able to adjust smaller things to optimize performance that comes through experience. It is recommended to use an optimization metric including different costs and types of errors.

Also, you should gain experience in dealing with open-ended questions, iterate different types of analysis methods and models by thinking out-of-the-box.

📍4) Advance your knowledge

Gain deeper theoretical understanding, experience, think about the obvious, and learn to solve real-world problems independently.

📍5) Earn data science certifications

Earning a data science certification helps you to build a strong and solid portfolio and land the job you want. It also helps to increase the industry knowledge and add value to skill sets. A certification helps you to:

Show your prospective employers that you have dedication toward the subject, and interest in continued learning and professional development.

Stay current about the industry practices, and give a competitive advantage while applying for jobs across the industries.

Comprehend the basics and advanced methodologies used in the industry for solving data science problems.

Here is a list of data science certifications you can choose to earn. It helps to improve your knowledge and skills pertaining to the industry.

The SDS™ certification is a multifaceted program covering a range of professional knowledge suitable for data scientists. It enables the data scientists to build longer-lasting skill sets, accept more challenging and bigger-impact roles. It proves your efficiency and potential to handle the onerous responsibilities of a data scientist. SDS™ is ranked among the world’s top 5 must-have global credentials for top-end careers by CIO Magazine.

To earn this certification, you should have passed the previous SAS certifications. You can learn to improve and manage data, work with data visualization tools, and many more. The other benefit is that the SAS data scientist certification does not expire.

This certification comprises two levels of training. As a first step, you should earn the associate certificate in big data analysis and data science foundations. Further, the specialist program covers everything such as visualization methods, HBase, Pig, Hadoop, advanced analytics, and natural language processing.

This is specifically designed for data scientists to use machine learning according to Microsoft. It enables them to train and deploy models that can solve business problems, learn predictive analytics, AI solutions, natural language processing, and more.

Moving forward, let’s understand the compensation for data scientists.

💲As per Glassdoor reports, the national average salary for data scientists is USD 1,15,383 per annum in the United States🗽

The job of a data scientist is to solve complex data problems creatively. It is not a task, but a journey to get the desired result.

Are you interested to take this journey?