Data science requires a comprehensive understanding of programming, statistics, and data visualization. This suite of courses covers the most important skills and concepts in these areas, helping students build a strong foundation for a successful career as a data scientist.
Explore graphical models and networks, and learn how to use them for representing complex relationships among variables. Gain a hands-on introduction to a popular programming language, Python. Visit for Data Science Training in Pune.
Programming
Data is the fuel that powers today’s business world and is key to corporate success. As such, it is important for businesses to know how to make sense of the information they receive and take action on it.
This is why it is essential for a Data Scientist to have programming skills. Data scientists are expected to understand and work with a variety of software applications, including Excel, Python, SQL, and Tableau. In addition, they are expected to be familiar with basic statistical concepts and algorithms.
This suite of courses will help learners build the foundational skills they need to become a Data Scientist. Through weekly sessions, learners will explore topics such as data visualization, statistics, programming, and machine learning. Learners will also gain hands-on experience with a real-world dataset to uncover defining characteristics and discover descriptive and inferential statistics. This course is ideal for professionals looking to expand their skill set and advance their careers.
Statistics
Statistics is essential for data scientists to understand and interpret. It allows them to discover patterns in data and evaluate the uncertainties of those discoveries.
It also helps data scientists communicate those findings in a way that makes them comprehensible to non-specialized data science partners, including full stack developers and DevOps engineers, as well as line of business (LOB) employees. This is a skill that’s sometimes called ‘good bedside manner,’ and it’s one of the most important aspects of a data scientist career.
The program also teaches you to work with different machine learning techniques, such as supervised and unsupervised learning, decision trees, logistic regression and more. You’ll be able to translate complex data sets into easily-digestible presentations with visualization tools like Tableau and ggplot. Plus, you can take the program on your own schedule without having to uproot your life to relocate to Virginia. You’ll get a complete set of course materials and the support you need to succeed from any computer with an Internet connection.
Machine Learning
As a machine learning specialist, you will be tasked with creating algorithms that can recognize patterns in large data sets. This data can then be used to answer important business questions, like predicting customer churn or forecasting sales growth. This is why it’s so important to have a strong grasp of how to train these algorithms and create accurate predictions using machine learning techniques.
The first step in this process is learning how to clean data and prepare it for machine learning. This is known as data wrangling. If your data isn’t cleaned as well as it could be, it may be prone to error that can impact the company’s bottom line.
Another key part of the machine learning process is understanding how to use different algorithms like decision trees, supervised machine learning and logistic regression. This is an essential skill that every machine learning professional needs to master. Getting this under your belt will also help you understand the reasoning behind the models you are constructing and make sure they are accurate.
Data Visualization
As the old saying goes, “a picture is worth a thousand words.” And when it comes to data analysis, this is especially true. Data visualization is the process of presenting data in visual format, using charts, graphs and maps to tell a story that makes sense for the viewer.
The most common use of data visualization is conveying changes over time, which can be accomplished by using line graphs or similar tools. Other examples include highlighting correlations between different variables (e.g., marketing spend vs. revenue) or determining the frequency of events by using histograms.
In addition, data visualization can also be used to highlight outliers and unusual groups, explore relationships and connections between data sets, spot local patterns, and evaluate modeling output. In this course, you’ll learn about statistically inclined visualization libraries such as Seaborn and Bokeh as well as geospatial visualizations such as heat and density maps. You’ll also practice your new skills with lab exercises, knowledge checks and assignments.
