Data analysis helps organizations make informed decisions by turning raw data into actionable insights. With businesses increasingly relying on data-driven strategies, the demand for skilled data analysts is rising. Learning data analysis equips you with the tools to uncover trends, solve problems, and add value in any field. This article lists the top data analysis courses that can help you build the essential skills needed to excel in this rapidly growing field.
Introduction to Data Analytics
This course provides a comprehensive introduction to data analysis, covering the roles of data professionals, data ecosystems, and Big Data tools like Hadoop and Spark. You’ll learn the fundamentals of gathering, cleaning, analyzing, and visualizing data. The course includes practical projects and guidance on career opportunities in data analysis, with no prior experience required.
Google Data Analytics Professional Certificate
This course, designed by Google, offers over 180 hours of training to prepare you for an entry-level data analytics job. It covers essential skills like data cleaning, problem-solving, and data visualization using tools like SQL, Tableau, and R Programming.
Introduction to Data Analytics
This course introduces the data analytics life cycle, focusing on key concepts like data integrity and the four types of data analytics: descriptive, diagnostic, predictive, and prescriptive. By completing the course, you’ll gain the skills to identify the appropriate data analytics strategy for various situations and understand your position within the analytics life cycle.
Google Advanced Data Analytics Professional Certificate
This professional certificate, designed by Google, offers advanced data analytics training over seven courses, building on existing data analytics skills. You’ll learn Python, Jupyter Notebook, Tableau, and machine-learning techniques through hands-on projects.
Meta Data Analyst Professional Certificate
This program prepares you for a data analytics career by building essential skills in Python, SQL, and statistics with no prior experience required. You’ll learn 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, allowing you to create a professional portfolio and earn a Meta Professional Certificate to showcase your expertise in data analysis.
Data Analytics Basics for Everyone
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 data gathering, wrangling, mining, analysis, and communication. It covers data structures, repositories, Big Data tools, and the ETL process. By the end of the course, learners will understand the career opportunities in Data Analytics and complete hands-on labs to reinforce their skills.
Data Analysis with Python
This course teaches essential data analysis skills using Python, covering topics like data collection, cleaning, manipulation, and visualization. You’ll learn to build and evaluate machine learning models, including regression models, using Python libraries like Pandas, Numpy, scipy, and scikit-learn. The course includes hands-on labs and projects to practice these skills.
Microsoft Power BI Data Analyst Professional Certificate
This program offers professional training in Microsoft Power BI, preparing you for a career as a Business Intelligence analyst. You’ll learn to transform 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.
Excel Basics for Data Analysis
This course provides a foundational understanding of Excel for data analysis, making it suitable for beginners with no prior experience. You’ll learn to work with spreadsheets, load data from various formats, and perform data wrangling, cleansing, and analysis using functions, filters, and pivot tables. The course emphasizes hands-on practice, allowing you to manipulate real data sets and complete a final project to showcase your skills.
Exploratory Data Analysis in Python
This course teaches the process of exploratory data analysis (EDA) in Python, using datasets on unemployment and plane ticket prices. You’ll learn 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, enabling you to create new features, balance categorical data, and generate hypotheses for further analysis.
Getting Started with Data Analytics on AWS
This course provides an overview of descriptive, diagnostic, predictive, and prescriptive data analysis techniques before focusing on descriptive analysis. You’ll apply your knowledge in a guided project using AWS CloudTrail logs and get introduced to Amazon Athena and QuickSight. The course also covers common data analysis scenarios and the benefits of cloud analytics and includes building a basic security dashboard to practice your skills.
Analyzing Data with Excel
This course offers fundamental training in using Excel for basic data analysis, suitable for aspiring Data Analysts, Data Scientists, or anyone needing Excel for business or research purposes. It covers data cleaning, wrangling, sorting, filtering, and pivot tables in both Microsoft Excel and Google Sheets.
Statistical Modeling and Computation in Applications
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 domains: epigenetics, criminal networks, economics, and environmental data.
Supply Chain Analytics in Python
This course introduces Supply Chain Analytics 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 enhance decision-making in supply chains. The course aims to improve supply chain decisions by leveraging optimization techniques and Python.
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