Data scientists work in tandem with data analysts, data engineers, business intelligence specialists, and data architects to create and maintain databases, analyze data, and communicate business insights. Their job is to identify the data analytics problems that offer the greatest opportunities to the organization.

Data science is an extremely broad field, entailing everything from cleaning data to deploying predictive models. It s rare for a data scientist to do it all. Most data scientists specialize in a specific function within the data processing cycle.

Data Scientist Role and Responsibilities

Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share insights with peers.

Some common data scientist responsibilities include:

Problem definition

The problem statement is the most crucial step towards solving any data analytics problem. Data scientists need to think of the problem statement in mathematical terms. For example, Why is product X underperforming and What data on user behavior might help explain it

Data collection and wrangling

Data wrangling is the process of cleaning, restructuring, and enriching raw data to make it analyzable. The primary goal of data wrangling is to reveal a deeper intelligence by gathering data from multiple sources and organizing the data for a broader analysis.

Exploratory analysis

Exploratory data analysis postpones any initial assumptions, hypotheses, or data models; instead, data scientists seek to uncover the underlying structure of the data, extract important variables, and detect outliers and anomalies.

Data processing

The data processing cycle refers to the set of operations used to transform the data into useful information. In this stage, the data is entered into a system, such as a CRM like Salesforce or a data warehouse like Redshift so that a data processing cycle can be established. Next, this process is deployed as a repeatable data model to enable long-term data analytics projects.

Model training and deployment

Data modeling represents the way data flows through a software application or the data architecture within an enterprise. It s almost like a blueprint that establishes relationships between different business entities to show how data is collected and stored.

Documentation, visualization, and presentation

Data scientists are expected to document their processes, providing sufficient descriptive information about their data for their own use as well as their colleagues and other data scientists in the future. Visualization is perhaps the most crucial aspect of the data function since computational statistics are only meaningful if they can be understood and acted upon by the organization.

There are several important Job Roles in Data Science most important one are :

• Data Analyst.
• Data Engineers.
• Database Administrator.
• Machine Learning Engineer.
• Data Scientist.
• Data Architect.
• Statistician.
• Business Analyst.

Most data scientists use the following core skills in their daily work:

Statistical analysis

Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection.

Machine learning

Implement algorithms and statistical models to enable a computer to automatically learn from data.

Computer science

Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.

Programming

Write computer programs and analyze large datasets to uncover answers to complex problems. Data scientists need to be comfortable writing code working in a variety of languages such as R, Python, and SQL.

Data storytelling

Communicate actionable insights using data, often for a non-technical audience.

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SAS Statistical Business Analyst Professional Certificate

For individuals who want to analyze big data with a variety of statistical analysis and predictive modeling techniques.

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Successful candidates should have experience in the following areas: Machine learning and predictive modeling techniques. Application of machine learning and predictive modeling techniques to big, distributed and in-memory data sets.

• Pattern detection.
• Experimentation in business.
• Optimization techniques.

Microsoft Certified: Data Scientist Associate

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

HBS Online Certificate in Business Analytics

Develop a data mindset and the analytical skills to interpret and communicate date while applying the concepts to real business problems.

You will learn how to apply Business Analytics tools and methods across different domains as well learn the inter-disciplinary approach to Analytics problem-solving across various industries.

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Data analysts prepare, process, and analyze data to help inform business decisions. They create visualizations to share their findings with stakeholders and provide recommendations driven by data.

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While we do recommend having some knowledge of at least one programming/scripting language or computing environment, many of our attendees come to us with little to no experience with programming.

Our pre-program coursework includes

• Tutorials on Programming basics
• Tutorials on Statistics

This would help you get ramped up for the program!

The pre-programming experience required by our course is great to have, but not necessary.

Our pre-program coursework includes tutorials on Introduction to R Programming to get you ramped up for the program! There you will learn to use basic programming constructs like how to assign variables, call functions, work with loops and decision tree logic.

In the program, we will assume that you do not come with any programming expertise and hence, will build your skills from scratch.

We work hard to ensure that no prior statistics knowledge is required. We will teach you all the basics you need to know before and during the bootcamps. We cover Statistical Finance concepts, correlations, hypothesis testing, and Bayes rule in the pre course bootcamp (online), all at a level appropriate for someone with no/little statistics experience.

Peer networking

In addition to the training program, you will have ample opportunity to network with the peers in your cohort. Prior to coming to the program, you will have access to your cohort where you can meet and engage with your peers before, during, and after the program.

Industry networking

Throughout the program, you will be taught and mentored by Data Scientists working/having worked with leading companies.

Industry networking

Communicate actionable insights using data, often for a non-technical audience.

Placement Leads

We have networks with leading industry participants including banks, financial institutions, consulting firms and analytics firms; you can leverage that network for connecting with the industry and finding suitable opportunities in Data Science. Once you are trained and ready, we will help you with placement leads that can help you land your dream role in Data Science.

Having said that, its entirely your performance and skill-sets that will define your journey in landing your dream job. We cannot guarantee that you will get a job after completing the program. Obtaining a job is strictly based off one s own skill sets. Upon completion of the program, we will provide you with a certificate that you can print and/or add to your LinkedIn profile.

Networking opportunities

You will have great networking opportunities at the program. Our industry faculty work in the industry in leading positions and that will provide a great opportunity for you to network. Besides, EduEdgePro, through its vast industry network, will help you reach out to the leading financial firms for placements.

Yes, this is an online program.

There are 2 versions

• Self-paced online
• Live synchronous training