The ability to gather, process and analyze data is one of the most sought-after skills in today’s job market. While some have questioned the value of certifications, they can help improve your marketability, increase your confidence and even provide networking opportunities.

The course will cover important topics in Data Science including coding, GitHub and version control, data acquisition, cleaning, machine learning algorithms, regression modeling and data visualization.

Machine Learning

One of the core components of data science is machine learning. It’s an area of expertise that can help you develop predictive models and make informed decisions about your business.

Whether you’re an experienced analyst or just getting started in the field, you can improve your skillset by exploring different machine learning algorithms and coding languages. It’s also helpful to gain familiarity with the underlying concepts of statistical analysis and data visualization, which are important for understanding what your data tells you. Check details for Data Science Training in Pune.

A good place to start is with a free online course in Machine Learning from the University of Washington, which provides you with the basic knowledge you need for your journey into data analytics. Alternatively, consider a structured program such as this one by Scaler (by InterviewBit) which offers lifetime access, hands-on practice with 45+ tools, real-world case studies and projects, $1,200 worth of IBM cloud credits and industry-recognized certifications from Simplilearn and Purdue University.

Deep Learning

A data scientist’s skill set is a vast and varied one, covering everything from coding to predictive analytics and more. UpGrad’s courses cater to all levels of experience, from those with no prior training who want to learn the basics, to professionals looking for advanced growth.

Typical prerequisites for this specialization may include college-level coursework in mathematics, algebra and programming. It also teaches students how to interpret and relay data information to stakeholders, as well as develop business intelligence solutions with big data.

This course focuses on deploying deep learning models, including convolutional neural networks (CNNs). Students will be taught how to build CNNs that can categorize text and images, as well as detect anomalies in real-time network protocol. It will then expand on this to introduce a number of other advanced machine learning algorithms, such as time-series analysis, recommendation engines and regression. By the end of this course, students will have a robust toolkit to help them solve complex problems in any industry.

Predictive Analytics

Turning raw data into valuable insights requires the help of professionals skilled in analyzing and making predictions with predictive analytics. Learn how predictive analytics helps organizations reduce risks, optimize operations and meet business objectives.

The five-step process of predictive analytics includes gathering and curating relevant data, establishing and training statistical models, selecting and testing sample data sets and validating or revising predictions. Predictive modeling can be used to analyze clickstream data, customer behavior, financial transactions, healthcare records and other types of information.

Data scientists use a variety of programming and analytics tools to collect, study, analyze and repackage data into user-friendly forms. A typical program may include coursework in database design, big data management, open-source software, and data visualization. A master’s degree program typically includes a capstone project or hands-on learning experience. An online bootcamp can also provide this opportunity. It’s important to find a program that prioritizes the skills you need in your career and aligns with your personal and professional goals.

Data Visualization

A key part of the data science process is visualizing data to help leaders understand and act on insights. This is especially important because the sheer volume of data that machine learning algorithms can gather can be difficult to sift through and comprehend at a glance. Data visualization helps highlight the most useful information in a dataset, making it easier to spot trends, patterns, outliers, and correlations.

As a data scientist, you’ll want to be comfortable using all of the programming languages and digital tools that are standard in the industry, including Excel, Python, SQL, R, and Tableau. You’ll also want to hone your domain knowledge, which refers to the ability to ask the right questions of your data and draw actionable conclusions.