Big Data Analytics from edX

What's inside

Learning objectives

How to develop algorithms for the statistical analysis of big data;
Knowledge of big data applications;

How to use fundamental principles used in predictive analytics;
Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.

How to develop algorithms for the statistical analysis of big data;
Knowledge of big data applications;
How to use fundamental principles used in predictive analytics;
Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.

Syllabus

Section 1: Simple linear regressionFit a simple linear regression between two variables in R;Interpret output from R;Use models to predict a response variable;Validate the assumptions of the model.

Section 2: Modelling dataAdapt the simple linear regression model in R to deal with multiple variables;Incorporate continuous and categorical variables in their models;Select the best-fitting model by inspecting the R output.

Section 3: Many modelsManipulate nested dataframes in R;Use R to apply simultaneous linear models to large data frames by stratifying the data;Interpret the output of learner models.

Section 4: ClassificationAdapt linear models to take into account when the response is a categorical variable;Implement Logistic regression (LR) in R;Implement Generalised linear models (GLMs) in R;Implement Linear discriminant analysis (LDA) in R.

Section 5: Prediction using modelsImplement the principles of building a model to do prediction using classification;Split data into training and test sets, perform cross validation and model evaluation metrics;Use model selection for explaining data with models;Analyse the overfitting and bias-variance trade-off in prediction problems.

Section 6: Getting biggerSet up and apply sparklyr;Use logical verbs in R by applying native sparklyr versions of the verbs.

Section 7: Supervised machine learning with sparklyrApply sparklyr to machine learning regression and classification models;Use machine learning models for prediction;Illustrate how distributed computing techniques can be used for “bigger” problems.

Section 8: Deep learningUse massive amounts of data to train multi-layer networks for classification;Understand some of the guiding principles behind training deep networks, including the use of autoencoders, dropout, regularization, and early termination;Use sparklyr and H2O to train deep networks.

Section 9: Deep learning applications and scaling upUnderstand some of the ways in which massive amounts of unlabelled data, and partially labelled data, is used to train neural network models;Leverage existing trained networks for targeting new applications;Implement architectures for object classification and object detection and assess their effectiveness.

Section 10: Bringing it all togetherConsolidate your understanding of relationships between the methodologies presented in this course, theirrelative strengths, weaknesses and range of applicability of these methods.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Designed for learners with some background in data science or programming

Designed for learners who want to enhance their programming and mathematical skills

Taught by Lewis Mitchell, Simon Tuke, and David Suter, who are recognized for their work in the field

Develops skills in cloud-based big data analysis, predictive analytics, and large-scale data analysis

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Big Data Analytics with these activities:

Review the book 'Applied Statistics and Probability for Engineers'

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This book provides a comprehensive overview of the fundamental concepts of statistics and probability, with a focus on applications in engineering

View Applied Statistics and Probability for... on Amazon

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Read the book carefully
Take notes on the key concepts
Work through the practice problems

Follow the tutorials on the 'StatQuest' YouTube channel

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These tutorials provide clear and concise explanations of statistical concepts, with a focus on practical applications

Browse courses on Statistics

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Find a tutorial on the topic you are interested in
Watch the tutorial carefully
Take notes on the key concepts

Review the fundamentals of statistics

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In order to understand the concepts covered in this course you should review the basic principles of statistics

Browse courses on Statistics

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Review the basics of probability distributions
Review the basics of statistical inference
Review the basics of regression analysis

Six other activities

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Create a cheat sheet of statistical formulas

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Creating a cheat sheet will help you organize your knowledge and make it easier to remember the formulas you need

Browse courses on Statistics

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List all of the statistical formulas you need to know
Organize the formulas into a logical order
Create a visually appealing cheat sheet

Participate in a peer study group

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Studying with peers can help you to learn the material more effectively and identify areas where you need additional support

Browse courses on Statistics

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Find a group of students who are also taking the course
Meet with the group regularly to discuss the material
Work together on practice problems

Practice solving statistical problems

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Solving statistical problems will help you develop your problem-solving skills and improve your understanding of the material

Browse courses on Statistics

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Find a collection of statistical problems
Attempt to solve the problems on your own
Check your solutions against the provided answer key

Mentor other students who are struggling with statistics

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Helping others to learn statistics will reinforce your own understanding of the material

Browse courses on Statistics

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Find a student who is struggling with statistics
Offer to help them with their studies
Meet with the student regularly to provide guidance and support

Start a project that uses statistical analysis to solve a real-world problem

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Working on a project will give you the opportunity to apply your statistical knowledge to a practical problem and develop your problem-solving skills

Browse courses on Statistics

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Identify a real-world problem that can be solved using statistical analysis
Collect data on the problem
Analyze the data using statistical methods
Develop a solution to the problem based on your analysis

Participate in a statistics competition

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Participating in a competition can help you to test your knowledge and skills, and to learn from others

Browse courses on Statistics

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Find a statistics competition that you are interested in
Register for the competition
Prepare for the competition by studying and practicing
Compete in the competition

Career center

Learners who complete Big Data Analytics will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst designs and builds systems and tools that retrieve, scrub, interpret, and visualize large amounts of raw data from a variety of sources. Part of this job is to develop new data mining techniques that will improve the quality and efficiency of data analysis. Data Analysts require knowledge of analytics, modeling, and databases, which are all skills that you will gain from taking the Big Data Analytics course.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists refine existing machine learning and statistical models using data provided from business analysts to identify risks and make predictions. They often wear many hats and are expected to be skilled in machine learning, data wrangling, communication, and programming. These are skills that will be refined taking the Big Data Analytics course.

See salaries and explore the career path for Data Scientist

Deep Learning Scientist

Deep Learning Scientists design and implement deep learning algorithms and models to solve complex problems in areas like image recognition and natural language processing. They use large data sets to train neural networks and other deep learning models. This course will introduce you to deep learning, enabling you to pursue a career as a Deep Learning Scientist.

See salaries and explore the career path for Deep Learning Scientist

Machine Learning Engineer

Machine Learning Engineers tune and implement algorithms that learn from big data, then develop and maintain the systems that power machine learning applications. They help companies stay competitive by improving the effectiveness of products and services through the use of data. While this course does not directly teach machine learning, you will learn about models that can be applied to machine learning.

See salaries and explore the career path for Machine Learning Engineer

Business Intelligence Analyst

Business Intelligence Analysts analyze data and trends to help businesses make informed decisions about strategy, operations, and marketing. They interpret complex data and communicate their insights to stakeholders in a clear and concise way. This course will give you the skills you need to become a successful Business Intelligence Analyst.

See salaries and explore the career path for Business Intelligence Analyst

Data Architect

Data Architects design, build, and maintain data architectures that support the needs of an organization. They work with business stakeholders to understand their data requirements and then design and implement data solutions that meet those requirements. This course will help you develop a solid understanding of the principles of data architecture.

See salaries and explore the career path for Data Architect

Database Administrator

Database Administrators maintain and optimize databases to ensure that they are running efficiently and securely. They also work with database users to provide support and training. This course will give you the skills you need to become a successful Database Administrator.

See salaries and explore the career path for Database Administrator

Data Engineer

Data Engineers build and maintain the systems that store and process data. They work with data scientists and other stakeholders to understand the data needs of the organization and then design and implement data solutions that meet those needs. This course will give you the skills you need to become a successful Data Engineer.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. They work with stakeholders to understand their needs and then design and implement software solutions that meet those needs. This course will help you develop a solid foundation in software engineering, which will be helpful if you want to pursue a career as a Software Engineer.

See salaries and explore the career path for Software Engineer

Statistician

Statisticians collect, analyze, and interpret data. They use their knowledge of statistics to draw conclusions about the world around them. This course will help you develop a solid foundation in statistics, which will be helpful if you want to pursue a career as a Statistician.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve problems in business and industry. They work with stakeholders to understand their needs and then develop and implement solutions that improve efficiency and productivity. This course will help you develop a solid foundation in operations research, which will be helpful if you want to pursue a career as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use data to make investment decisions. They analyze financial data to identify trends and make recommendations about which investments to buy or sell. This course will help you develop a solid foundation in financial analysis, which will be helpful if you want to pursue a career as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Market Research Analyst

Market Research Analysts conduct research to understand consumer behavior and trends. They use their findings to help businesses make informed decisions about product development and marketing. This course will help you develop a solid foundation in market research, which will be helpful if you want to pursue a career as a Market Research Analyst.

See salaries and explore the career path for Market Research Analyst

Actuary

Actuaries use mathematics and statistics to assess risk and uncertainty. They work with insurance companies and other organizations to develop and implement strategies to manage risk. This course will help you develop a solid foundation in actuarial science, which will be helpful if you want to pursue a career as an Actuary.

See salaries and explore the career path for Actuary

Economist

Economists study how people make decisions in the face of scarcity. They use economic models to analyze data and make predictions about the economy. This course will help you develop a solid foundation in economics, which will be helpful if you want to pursue a career as an Economist.

See salaries and explore the career path for Economist

Big Data Analytics

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