Data analytics helps organizations make informed decisions by transforming raw data into actionable insights. As businesses increasingly rely on data-driven strategies, the demand for qualified data analysts is increasing. Learning data analytics provides you with the tools to uncover trends, solve problems, and add value in any area. This article lists the best data analytics courses that can help you learn the essential skills needed to excel in this rapidly growing field.
This course provides a comprehensive introduction to data analytics, covering the roles of data professionals, data ecosystems, and Big Data tools like Hadoop and Spark. You will learn the fundamentals of collecting, cleaning, analyzing and visualizing data. The course includes hands-on projects and guidance on career opportunities in data analytics, with no prior experience required.
This course, designed by Google, provides over 180 hours of training to prepare you for an entry-level data analysis role. It covers essential skills such as data cleaning, problem solving and data visualization using tools such as 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. By completing the course, you will gain the skills to identify the appropriate data analysis strategy for various situations and understand your position in the analytics lifecycle.
This professional certificate, designed by Google, provides advanced data analytics training across seven courses, building on existing data analytics skills. You will learn Python, Jupyter Notebook, Tableau, and machine learning techniques through hands-on projects.
This program prepares you for a career in data analysis by developing essential skills in Python, SQL, and statistics with no prior experience required. You will learn how to collect, process and analyze data using tools such as Tableau and apply the OSEMN framework to solve analysis problems. The program includes hands-on projects, allowing you to create a professional portfolio and earn a Meta Professional certificate to showcase your data analysis expertise.
This IBM course introduces learners 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 collecting, managing, data mining, analysis and reporting. It covers data structures, repositories, Big Data tools and the ETL process. At the end of the course, learners will understand career opportunities in the field of data analytics and complete hands-on workshops to strengthen their skills.
This course teaches essential data analysis skills using Python, covering topics such as data collection, cleaning, manipulation, and visualization. You will learn how to create 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 provides professional training in Microsoft Power BI, preparing you for a career as a Business Intelligence analyst. You will learn how to turn data into insights, create reports and dashboards, and use DAX for calculations. The program includes hands-on projects and a capstone project, simulating 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 different formats, and perform data management, cleaning, and analysis using functions, filters, and pivot tables. The course emphasizes hands-on practice, allowing you to manipulate real-world datasets and complete a final project to showcase your skills.
This course teaches the process of exploratory data analysis (EDA) in Python, using unemployment and airline ticket price datasets. You’ll learn how to summarize, clean, and visualize data with Seaborn, exploring relationships between variables and handling missing values. The course also shows how to integrate EDA results into data science workflows, allowing you to create new features, balance categorical data, and generate hypotheses for further analysis.
This course provides an overview of descriptive, diagnostic, predictive, and prescriptive data analysis 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 analysis scenarios and the benefits of cloud analytics and includes creating a basic security dashboard to practice your skills.
This course provides fundamental 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 cleaning, managing, sorting, filtering and pivoting data in Microsoft Excel and Google Sheets.
This course equips learners 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 areas: 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 distribution, with emphasis on sensitivity analysis and simulation testing for improve decision-making in supply chains. The course aims to improve supply chain decisions by leveraging optimization techniques and Python.
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Shobha is a data analyst with a proven track record of developing innovative machine learning solutions that drive business value.
