Best Data Analytics Courses – MarkTechPost

Data analytics helps organizations make informed decisions by turning raw data into actionable insights. As businesses increasingly rely on data-driven strategies, the demand for skilled data analysts is increasing. Learning data analytics provides you with the tools to uncover trends, solve problems, and add value in any field. This article lists the best data analytics courses that can help you develop the essential skills needed to excel in this rapidly growing field.

This course offers a comprehensive introduction to data analytics and covers the roles of data professionals, data ecosystems, and Big Data tools like Hadoop and Spark. You’ll learn the basics of data collection, cleaning, analysis, and visualization. The course includes hands-on projects and guidance on career opportunities in data analytics—no prior experience required.

Designed by Google, this course offers over 180 hours of training to prepare you for an entry-level data analytics job. It covers essential skills such as data cleaning, problem solving, and data visualization using tools like SQL, Tableau, and R Programming.

This course introduces the data analytics lifecycle, focusing on key concepts such as data integrity and the four types of data analytics: descriptive, diagnostic, predictive, and prescriptive. Upon completion of the course, you will gain the skills to identify the appropriate data analytics strategy for various situations and understand where you fit within the analytics lifecycle.

This professional certificate, designed by Google, offers advanced data analytics training in seven courses, which build on existing data analytics skills. You'll learn Python, Jupyter Notebook, Tableau, and machine learning techniques through hands-on projects.

This program prepares you for a career in data analytics by developing essential skills in Python, SQL, and statistics with no prior experience required. You'll learn how to collect, process, and analyze data using tools like Tableau and apply the OSEMN framework to solve analytics problems. The program includes hands-on projects that will allow you to build a professional portfolio and earn a Meta Professional Certificate to demonstrate your data analytics expertise.

This IBM course introduces students to the components of a modern data ecosystem, the roles of data analysts, data scientists, and data engineers, and the tasks they perform, such as data collection, management, mining, analysis, and communication. It covers data structures, repositories, Big Data tools, and the ETL process. Upon completion of the course, students will understand career opportunities in the data analytics field and will complete hands-on labs to reinforce their skills.

This course teaches essential data analysis skills using Python and covers topics such as data collection, cleaning, manipulation, and visualization. You will learn how to build and evaluate machine learning models, including regression models, using Python libraries such as Pandas, Numpy, scipy, and scikit-learn. The course includes hands-on labs and projects to practice these skills.

This program offers professional training in Microsoft Power BI, preparing you for a career as a business intelligence analyst. You'll learn how to transform data into insights, create reports and dashboards, and use DAX to perform calculations. The program includes hands-on projects and a capstone project, which simulates real-world scenarios.

This course provides a basic understanding of Excel for data analysis, making it suitable for beginners with no prior experience. You will learn how to work with spreadsheets, load data from various formats, and perform data manipulation, cleaning, and analysis using functions, filters, and pivot tables. The course emphasizes practice, allowing you to manipulate real data sets and complete a capstone project to demonstrate your skills.

This course teaches the process of exploratory data analysis (EDA) in Python, using datasets on unemployment and airline ticket prices. You will learn how to summarize, clean, and visualize data with Seaborn, exploring relationships between variables and handling missing values. The course also demonstrates how to incorporate EDA findings into data science workflows, allowing you to build new features, balance categorical data, and generate hypotheses for further analysis.

This course provides an overview of descriptive, diagnostic, predictive, and prescriptive data analytics techniques before focusing on descriptive analytics. You’ll apply your knowledge in a guided project using AWS CloudTrail logs and learn about amazon Athena and QuickSight. The course also covers common data analytics scenarios and the benefits of cloud analytics, and includes building a basic security dashboard to practice your skills.

This course provides foundational training in using Excel for basic data analysis, suitable for future data analysts, data scientists, or anyone who needs Excel for business or research purposes. It covers data cleaning, organizing, sorting, filtering, and pivot tables in both Microsoft Excel and Google Sheets.

This course provides students with multidisciplinary skills in data science, combining mathematics, statistics, machine learning, and programming with domain-specific knowledge. It covers hypothesis testing, regression, and gradient descent, followed by analysis techniques in four domains: epigenetics, criminal networks, economics, and environmental data.

This course introduces supply chain analysis using Python's PuLP library for linear programming optimization. It covers modeling and solving supply chain optimization problems such as facility location and demand allocation, with a focus on sensitivity analysis and simulation testing to improve decision making in supply chains. The course aims to improve supply chain decisions by leveraging optimization techniques and Python.

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