Coursera

4.91

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved...

4.8

Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystemConduct an analytical thinking self assessment giving specific examples of the application of analytical thinkingDiscuss the role of spreadshe...

4.71

Explain how each step of the problem-solving road map contributes to common analysis scenarios. Discuss the use of data in the decision-making process. Demonstrate the use of spreadsheets to complete basic tasks of the data...

4.53

Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand...

4.88

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the...

4.76

Explain factors to consider when making decisions about data collectionDiscuss the difference between biased and unbiased dataDescribe databases with references to their functions and componentsDescribe best practices for organizing data

4.68

Define data science and its importance in today’s data-driven world. Describe the various paths that can lead to a career in data science. Describe the advice given by seasoned data science professionals to data scientists...

4.63

Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results. U​se SQL commands to filter, sort, and summarize data. Create an analysis...

4.8

Identify the key components of the ML lifecycle and pipeline and compare the ML modeling iterative cycle with the ML product deployment cycle. Understand how performance on a small set of disproportionately important examples may...

4.75

Explain what Data Analytics is and the key steps in the Data Analytics process. Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst. Describe the...

4.81

Define data integrity with reference to types of integrity and risk to data integrityApply basic SQL functions for use in cleaning string variables in a databaseDevelop basic SQL queries for use on databasesDescribe the process...

4.86

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn...

4.51

Describe the languages, tools, and data used by data scientists, including IBM tools focused on data science. Describe the features of Jupyter Notebook and RStudio IDE that make them popular for data science projects. Create...

4.61

Explain Python Basics including Types, Expressions, and Variables. Describe Data Structures in Python including Lists, Tuples, Dictionaries, Sets. Apply Python programming using Branching, Loops, Functions, Objects & Classes. Work with data in Python...

4.63

Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.

4.63

Discuss the importance of organizing your data before analysis with references to sorts and filtersDemonstrate an understanding of what is involved in the conversion and formatting of dataApply the use of functions and syntax to...

4.85

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more....

4.78

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you...

4.42

Identify responsible data collection for building a fair ML production system. Implement feature engineering, transformation, and selection with TensorFlow ExtendedUnderstand the data journey over a production system’s lifecycle and leverage ML meta...

4.75

Describe the fundamentals of spreadsheet applications Perform basic spreadsheet tasks including navigation, data entry, and using formulas. Employ data quality techniques to import and clean data in Excel. Analyze data in spreadsheets by using filter...