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Google Data Analytics Professional Certificate

Google Data Analytics Professional Certificate

If you’re searching for the best courses in data science, the Google Data Analytics Certificate on Coursera is a fantastic option.

This program is a comprehensive suite of eight courses that cover everything from the foundations of data analytics to the application of R programming.

The first course, “Foundations: Data, Data, Everywhere,” is a great starting point. It introduces you to the world of data analytics and provides an overview of the key analytical skills and tools you’ll need, such as data cleaning, data analysis, and data visualization.

The course is taught by current Google data analysts, which gives you a real-world perspective on the subject matter.

The second course, “Ask Questions to Make Data-Driven Decisions,” builds on the first course and focuses on the importance of questioning and decision-making in data analysis, which is often overlooked in similar courses.

The third course, “Prepare Data for Exploration,” introduces you to new topics that will help you gain practical data analytics skills.

You’ll learn how to use tools like spreadsheets and SQL to extract and make use of the right data for your objectives.

The fourth course, “Process Data from Dirty to Clean,” teaches you how to check and clean your data using spreadsheets and SQL.

This course stands out because it focuses on the importance of data integrity and provides you with hands-on ways to accomplish common data analyst tasks.

The fifth course, “Analyze Data to Answer Questions,” explores the “analyze” phase of the data analysis process.

You’ll learn how to organize and format your data using spreadsheets and SQL to help you look at and think about your data in different ways.

The sixth course, “Share Data Through the Art of Visualization,” shows you how data visualizations can help bring your data to life.

You’ll also explore Tableau, a data viz platform that will help you create effective visualizations for your presentations.

The seventh course, “Data Analysis with R Programming,” introduces you to the programming language known as R.

I would prefer you take a course in Python, but R is still a great language to learn for data analysis.

It’s the second most popular programming language for data analysis, after Python, and this course will teach you how to use it for data analysis.

You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways.

The final course, “Google Data Analytics Capstone: Complete a Case Study,” gives you the opportunity to apply what you’ve learned in a real-world context.

You’ll choose an analytics-based scenario and complete a case study, which is a great way to showcase your skills to potential employers.

Overall, the Google Data Analytics Certificate is a comprehensive and practical program that equips you with the skills needed to apply for introductory-level data analyst jobs.

The courses are well-structured, engaging, and taught by industry professionals.

Google Advanced Data Analytics Professional Certificate

Google Advanced Data Analytics Professional Certificate

This program is a comprehensive suite of seven courses that cover everything from the foundations of data science to advanced machine learning techniques.

It’s a great option to consider after completing the previous course, as it builds on the skills and knowledge you’ve gained in that program.

The first course, Foundations of Data Science, sets the stage for your learning journey.

It introduces you to the role of data professionals and the project workflow PACE (Plan, Analyze, Construct, Execute). This course is unique because it’s taught by Google employees who work in the field, providing you with real-world examples and hands-on activities.

Next, you’ll dive into Python programming in the Get Started with Python course.

Python is a powerful tool for data analysis, and this course will teach you the basics, from variables and data types to loops and data structures.

The third course, Go Beyond the Numbers: Translate Data into Insights, teaches you how to find the story within data and communicate it effectively. You’ll practice exploratory data analysis and learn how to create compelling data visualizations.

The Power of Statistics, the fourth course, introduces you to the use of statistics in data science.

You’ll explore key concepts like probability, sampling, confidence intervals, and hypothesis testing, all while using Python for statistical analysis.

In the fifth course, Regression Analysis: Simplify Complex Data Relationships, you’ll learn about different methods of data modeling, including linear regression, analysis of variance (ANOVA), and logistic regression.

This course will help you understand how data professionals use regression analysis to discover relationships between variables and identify key factors affecting business performance.

The sixth course, The Nuts and Bolts of Machine Learning, delves into the world of machine learning.

You’ll learn about supervised and unsupervised learning, and how to apply different machine learning models to business problems.

Finally, the Google Advanced Data Analytics Capstone course gives you the opportunity to apply everything you’ve learned in a capstone project.

This is a great way to showcase your new skills and knowledge.

IBM Data Science Professional Certificate

IBM Data Science Professional Certificate

The first course, “What is Data Science?” provides a solid foundation for understanding the field of data science.

It’s a great starting point for anyone interested in this field, offering insights into the work of data scientists and the importance of data science in today’s data-driven world.

The course has a high rating of 4.7, indicating that many learners found it valuable.

Next, “Tools for Data Science” introduces you to the essential tools used by data science professionals.

You’ll get hands-on experience with tools like Jupyter Notebooks, RStudio IDE, Git, GitHub, and Watson Studio.

This course is highly practical, allowing you to test each tool and run simple code in Python, R, or SQL.

The “Data Science Methodology” course teaches you how to think and work like a successful data scientist.

You’ll learn about the six-stage CRISP-DM data science methodology, which is widely used by professional data scientists.

“Python for Data Science, AI & Development” is a beginner-friendly course that takes you from zero to programming in Python in a matter of hours.

You’ll learn about Python basics, different data types, and how to use Python libraries such as Pandas, Numpy & Beautiful Soup.

The “Python Project for Data Science” is a mini-course designed to kickstart your portfolio by demonstrating your foundational Python skills for working with data.

You’ll work on a real-world data set and scenario to identify patterns and trends, showcasing your proficiency with Python and data science tools.

One skill that can never be overlooked in data science is SQL.

From the most senior machine learning engineers to the most junior data analysts, SQL is a skill that shows up in almost every data science job description.

“Databases and SQL for Data Science with Python” equips you with a working knowledge of SQL, a powerful language used for communicating with and extracting data from databases. You’ll learn SQL from the basics to advanced concepts like JOINs.

“Data Analysis with Python” takes you from the basics of data analysis with Python to building and evaluating data models.

You’ll learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations.

“Data Visualization with Python” teaches you how to effectively visualize both small and large-scale data using the very popular Python libraries Matplotlib, Seaborn, and Folium.

You’ll learn to create various types of basic and advanced graphs and charts, and even interactive dashboards.

“Machine Learning with Python” dives into the world of Machine Learning (ML) using Python.

You’ll learn about different machine learning algorithms and get hands-on experience building, evaluating, and comparing several Machine Learning models using the popular Scikit-learn library.

Finally, the “Applied Data Science Capstone” is the final course in the IBM Data Science Professional Certificate.

This capstone project course gives you the chance to apply what you’ve learned in the previous courses to a real-world scenario.

Google Business Intelligence Professional Certificate

Google Business Intelligence Professional Certificate

This program is a comprehensive, three-course journey that equips you with the skills needed to apply for entry-level roles as a business intelligence (BI) professional.

I decided to add this course because not only it’s a great way to enter the data field, but also because the “data science” job descriptions today often mean business intelligence and analytics jobs.

The first course, Foundations of Business Intelligence, introduces you to the role of BI professionals and the tools they use.

You’ll learn how to make key distinctions between business intelligence and data analysis, and how to use data in business processes and decision-making.

The course is led by Google employees who work in BI, providing real-world examples and hands-on activities.

With a high rating of 4.8 from over 600 raters, it’s clear that this course delivers on its promises.

Next up is The Path to Insights: Data Models and Pipelines.

This course dives deeper into the technical aspects of BI, focusing on data modeling and extract, transform, load (ETL) processes.

You’ll learn how to determine which data models are appropriate for different business requirements, and how to design an ETL process that meets organizational and stakeholder needs.

This course also boasts a high rating of 4.7, indicating that learners find it valuable and effective.

The final course, Decisions, Decisions: Dashboards and Reports, is the cherry on top.

It allows you to apply your understanding of stakeholder needs, plan and create BI visuals, and design reporting tools, including dashboards.

You’ll learn how to produce charts that represent BI data monitored over time, and how to design BI presentations to share insights with stakeholders.

This course has the highest rating of the three, at 4.8, showing that it’s a strong finish to the program.

One of the standout features of this program is that it’s taught by Google employees who currently work in BI.

This means you’re learning from professionals who are actively involved in the field, and can provide real-world examples and insights.

The hands-on activities that simulate job tasks are another unique feature, giving you practical experience that you can apply in your future career.

Applied Data Science Specialization

Applied Data Science Specialization

This series of courses is designed to take you from a beginner to a proficient data scientist, all while using the popular programming language Python.

The first course, Python for Data Science, AI & Development, is beginner-friendly and self-paced, so you can learn at your own speed.

You’ll get a solid foundation in Python basics, data types, and structures. Plus, you’ll get hands-on experience with Python libraries like Pandas, Numpy, and Beautiful Soup.

By the end of the course, you’ll feel comfortable creating basic programs and automating tasks using Python.

Next up is the Python Project for Data Science.

This mini-course is a great opportunity to apply what you’ve learned in a real-world scenario.

You’ll work on a project that involves extracting data, web scraping, visualizing data, and creating a dashboard.

This project will showcase your proficiency with Python and its libraries, giving you a tangible piece of work to add to your portfolio.

The third course, Data Analysis with Python, takes you deeper into the world of data science.

You’ll learn how to import, clean, and wrangle data, perform exploratory data analysis, and create meaningful data visualizations.

You’ll also get to build and evaluate machine learning regression models and pipelines.

The fourth course, Data Visualization with Python, focuses on one of the most important skills for data scientists: the ability to visualize data in a compelling way.

This course will give you the tools (Matplotlib, Seaborn, Folium) to take raw data and present it in a way that’s easy to understand and engaging.

You’ll learn to create various types of graphs and charts, and even interactive dashboards.

Finally, the Applied Data Science Capstone is the culmination of all your hard work.

In this course, you’ll assume the role of a Data Scientist working for a startup.

You’ll follow the Data Science methodology, working with real-world datasets, and developing machine learning models.

This capstone project is a great way to showcase your skills to potential employers.

IBM Data Analyst Professional Certificate

IBM Data Analyst Professional Certificate

This comprehensive program offers a series of courses that cover everything from the basics of data analysis to advanced techniques and tools.

The first course, Introduction to Data Analytics, provides a solid foundation in the field, explaining the roles of data analysts, data scientists, and data engineers.

You’ll learn about the data ecosystem, including databases, data warehouses, and big data platforms.

By the end of the course, you’ll understand the fundamentals of the data analysis process, including gathering, cleaning, analyzing, and sharing data.

Next, the Excel Basics for Data Analysis course will give you a basic working knowledge of Excel and how to use it for analyzing data.

You’ll learn how to perform basic data wrangling and cleansing tasks using functions, and expand your knowledge of data analysis through the use of filtering, sorting, and pivot tables.

The Data Visualization and Dashboards with Excel and Cognos course will teach you how to create compelling data visualizations and dashboards.

You’ll learn how to use business intelligence (BI) tools like Cognos Analytics to create interactive dashboards.

This course is a great way to learn how to communicate your data analysis findings effectively.

The Python for Data Science, AI & Development course is a comprehensive introduction to Python, the most popular languages in the data science world.

You’ll learn about Python basics, data structures, and how to use Python libraries such as Pandas, Numpy & Beautiful Soup.

The Python Project for Data Science course is a mini-course designed to help you demonstrate your Python skills.

You’ll work on a real-world data set and scenario, performing tasks such as extracting data, web scraping, visualizing data, and creating a dashboard.

The Databases and SQL for Data Science with Python course is a must for any data professional.

You’ll learn SQL from the ground up, from basic SELECT statements to advanced concepts like JOINs.

The Data Analysis with Python course will take you from the basics of data analysis with Python to building and evaluating data models.

You’ll learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations.

The Data Visualization with Python course will teach you many ways to effectively visualize both small and large-scale data.

You’ll learn to create various types of basic and advanced graphs and charts, and create interactive dashboards.

Finally, the IBM Data Analyst Capstone Project will allow you to apply everything you’ve learned in a real-world context.

You’ll perform various tasks that professional data analysts do as part of their jobs, including data collection, data wrangling, exploratory data analysis, statistical analysis, data visualization, and interactive dashboard creation.

Introduction to Data Science Specialization

Introduction to Data Science Specialization

The first course, “What is Data Science?” is a fantastic starting point.

It’s not just about learning the basics, but also understanding the significance of data science in today’s world.

You’ll get to hear from seasoned data science professionals who share their insights and experiences, which is a unique feature that sets this course apart from others.

With a high rating of 4.7 from over 65,000 raters, it’s clear that this course delivers on its promises.

Next up is “Tools for Data Science”. This course is all about getting hands-on with the tools that data science professionals use daily.

You’ll get to work with Jupyter Notebooks, RStudio IDE, Git, GitHub, and Watson Studio, among others.

The course provides plenty of opportunities for practical application, allowing you to test each tool and run simple code in Python, R, or SQL.

The third course, “Data Science Methodology”, is a shortcut to thinking and working like a successful data scientist.

You’ll learn about two notable data science methodologies and how to apply them. This course is unique in its focus on methodology, which is often overlooked in other data science courses.

Finally, “Databases and SQL for Data Science with Python” is a must for any aspiring data professional.

SQL is a powerful language used for communicating with and extracting data from databases, and this course will teach you everything you need to know.

You’ll get to work with real databases on the Cloud and use real data science tools, ensuring you’re well-prepared for the real world.

Learn SQL Basics for Data Science Specialization

Learn SQL Basics for Data Science Specialization

As I said before, SQL is a language that spans the work of junior data analysts to senior machine learning engineers.

This specialization is a comprehensive package that offers a deep dive into it.

The first course, SQL for Data Science, is designed for beginners and will give you a solid foundation in SQL.

You’ll learn how to write both simple and complex queries, work with different types of data, and create new tables.

This course is highly rated with a score of 4.6 out of 5, and it’s easy to see why. It’s comprehensive, easy to follow, and provides real-world programming assignments for practice.

The second course, Data Wrangling, Analysis and AB Testing with SQL, allows you to apply the SQL skills you’ve learned to real data science case studies.

You’ll learn how to clean data, perform optimal JOIN operations, and segment and analyze data using windowing functions.

As the name implies, this course also covers A/B testing, a popular method, widely used in industry, for finding the best version of a product or service.

The third course, Distributed Computing with Spark SQL, is all about “big data”.

This course is unique as it focuses on distributed computing using Apache Spark, a widely used open-source standard for working with large datasets.

You’ll learn about Spark architecture, how to optimize Spark SQL, and how to build reliable data pipelines.

The final course, SQL for Data Science Capstone Project, gives you the opportunity to apply everything you’ve learned in a real-world context.

You’ll develop a project proposal, perform exploratory analysis, develop metrics, and present your findings.

This course is a great way to consolidate your learning and create a portfolio-worthy piece.

Practical Data Science on the AWS Cloud Specialization

Practical Data Science on the AWS Cloud Specialization

This specialization is a comprehensive package that offers a deep dive into practical data science, with the added bonus of focusing on handling massive datasets and deploying data science projects in the cloud.

The first course, “Analyze Datasets and Train ML Models using AutoML,” is a solid foundation for anyone interested in exploratory data analysis (EDA) and automated machine learning (AutoML).

AutoML is a fast way to build and deploy machine learning models in an automated fashion that has been growing in popularity in recent years.

It’s a hands-on course where you’ll work with Amazon SageMaker to analyze datasets for statistical bias, transform data into machine-readable features, and train a multi-class text classifier.

The second course, “Build, Train, and Deploy ML Pipelines using BERT,” takes you a step further into the world of natural language processing.

You’ll learn to build an end-to-end machine learning pipeline using the state-of-the-art BERT model, which is widely used in text mining in industry.

Learners have praised the course for its in-depth coverage of ML Pipelines and MLOps, and model training and deployment with BERT.

The third course, “Optimize ML Models and Deploy Human-in-the-Loop Pipelines,” is all about performance improvement and cost reduction.

You’ll learn to tune model accuracy, compare prediction performance, and generate new training data with human intelligence.

This course is unique in its focus on human-in-the-loop pipelines with lessons on distributed model training, hyperparameter tuning, and A/B testing.

Data Science Specialization By Johns Hopkins University

JHU Data Science Specialization

This specialization is great if you are particularly interested in R programming.

Starting with “The Data Scientist’s Toolbox”, you’ll get a solid introduction to the main tools and ideas in the data scientist’s toolbox.

This course is a great starting point, providing a conceptual introduction to turning data into actionable knowledge. You’ll learn how to set up R, R-Studio, Github, and other useful tools.

Next up is “R Programming”, where you’ll learn how to program in R and use it for effective data analysis.

This course is highly rated, with a focus on practical issues in statistical computing. You’ll learn how to configure statistical programming software and make use of R loop functions and debugging tools.

“Getting and Cleaning Data” covers the basics of obtaining and cleaning data, which is a crucial step before any data analysis can be performed.

You’ll learn how to obtain usable data from the web, APIs, and databases, and apply data cleaning basics to make data “tidy”.

The “Exploratory Data Analysis” covers essential techniques for summarizing data.

You’ll learn how to use advanced graphing systems such as the Lattice system and apply cluster analysis techniques to locate patterns in data.

“Reproducible Research” focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner.

This course is becoming increasingly important as data analyses become more complex. You’ll learn how to organize data analysis to help make it more reproducible and publish reproducible web documents using Markdown.

“Statistical Inference” is a practical course that presents the fundamentals of inference in a practical approach for getting things done.

You’ll learn how to use p-values, confidence intervals, and permutation tests, and make informed data analysis decisions.

“Regression Models” is a comprehensive course that covers regression analysis, least squares, and inference using regression models.

You’ll learn how to use regression analysis, understand ANOVA and ANCOVA model cases, and investigate analysis of residuals and variability.

“Practical Machine Learning” covers the basic components of building and applying prediction functions with an emphasis on practical applications.

You’ll learn how to use the basic components of building and applying prediction functions and describe machine learning methods such as regression or classification trees.

“Developing Data Products” is a unique course that covers the basics of making your work available to decision-makers through data products using Shiny, R packages, and interactive graphics.

You’ll learn how to develop basic applications and interactive graphics using GoogleVis and create a data product that tells a story to a mass audience.

Finally, the “Data Science Capstone” allows you to apply everything you’ve learned in a real-world project.

This is a great opportunity to create a usable/public data product that can be used to show your skills to potential employers.

Data Science: Foundations using R Specialization

Data Science: Foundations using R Specialization

This is another specialization that focuses on R programming.

The first course, The Data Scientist’s Toolbox, introduces you to the main tools and ideas that data scientists work with.

You’ll learn how to set up R, R-Studio, Github, and other useful tools. You’ll also gain a solid understanding of the data, problems, and tools that data analysts use.

Next up is R Programming.

This course will teach you how to program in R and how to use R for effective data analysis. You’ll learn how to install and configure software necessary for a statistical programming environment.

The course also covers practical issues in statistical computing, including programming in R, reading data into R, accessing R packages, writing R functions, debugging, and profiling R code.

The third course, Getting and Cleaning Data, focuses on the basics of obtaining and cleaning data.

You’ll learn how to obtain data from various sources, including the web, APIs, and databases. You’ll also learn how to make data “tidy”, which can dramatically speed up downstream data analysis tasks.

The fourth course, Exploratory Data Analysis, covers the essential techniques for summarizing data.

You’ll learn about the plotting systems in R and the basic principles of constructing data graphics. You’ll also learn about common multivariate statistical techniques used to visualize high-dimensional data.

The final course, Reproducible Research, focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner.

You’ll learn how to organize data analysis to make it more reproducible and how to write up a reproducible data analysis using knitr.