Statistics and Data Analysis with Excel, Part 1 from Coursera

What's inside

Syllabus

WELCOME!

Welcome to the course! In this module, you will orient yourself to the course policies and will learn a few of the basics related to statistics.

Descriptive Statistics and Graphical Representation of Data

During Week 2, you will learn how to calculate population and sample statistics as well as quartiles and percentiles. Data visualization is important in the field of statistics - you will learn all about histograms, which are used for presenting univariate data in graphical format, as well as scatter plots and column plots. You will learn how to visualize univariate data in a box plot, which is a nice technique for identifying outliers. Finally, you will learn how to clean and transform data and use robust estimators in data sets that are highly affected by outliers.

Probability

In Week 3, you will learn all about probability and counting techniques. A thorough understanding of probability is paramount for the study of statistics. There are several rules and axioms that govern probability, and you will explore these rules in several screencasts. Finally, you will learn about conditional probability, which is the foundation for Bayes' Theorem.

Discrete Probability Distributions

Week 4 focuses on discrete probability distributions, in which the random variable is constrained to discrete values. Discrete probability distributions allow statisticians to make probabilistic predictions related to discrete stochastic models. These distributions include the binomial, geometric, negative binomial, hypergeometric, multinomial, and Poisson distributions.

Continuous Probability Distributions

Building on what you learned about probability distributions in Week 4, you will explore continuous random variables and continuous probability distributions in Week 5. These distributions include the common normal distribution and standard normal distribution, but we'll also delve into the exponential distribution, gamma distribution, and others. These distributions allow us to make probabilistic predictions related to stochastic models.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for beginners with no prior statistics knowledge

Focuses on descriptive statistics, probability, and discrete and continuous probability distributions, which are fundamental concepts in data science, data analytics, and machine learning

Utilizes Microsoft Excel for assignments, which is widely used in industry

Part of a two-part course sequence, providing a comprehensive foundation in statistics

Covers a range of topics, including probability, counting techniques, discrete probability distributions, and continuous probability distributions

Course materials not specified, which may limit potential learners

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 Statistics and Data Analysis with Excel, Part 1 with these activities:

Review probability fundamentals

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Ensure you understand the fundamentals of probability, which is foundational to studying statistics.

Browse courses on Conditional Probability

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Review the axioms and rules of probability
Practice calculating probabilities using counting techniques
Practice applying probability rules to solve problems

Recall Descriptive Statistics Concepts

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Brush up on descriptive statistics to prepare for the course's exploration of data analysis.

Browse courses on Descriptive Statistics

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Review measures of central tendency (mean, median, mode)
Practice interpreting box plots and histograms
Review measures of variability (range, variance, standard deviation, quartiles)

Read 'Data Manipulation with R'

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Enhance your understanding of data manipulation and transformation techniques.

View Efficient R Programming: A Practical Guide to... on Amazon

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Read chapters 1-3 to familiarize yourself with the basics of data manipulation in R
Complete the practice exercises at the end of each chapter

Five other activities

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Practice Probability Calculations

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Reinforce your understanding of probability by practicing calculations.

Browse courses on Probability

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Find a set of practice problems on probability
Solve the problems, checking your answers against the provided solutions
Review the problems you answered incorrectly and identify areas where you need more practice

Learn about Statistical Distributions

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Expand your understanding of statistical distributions and their applications.

Browse courses on Probability Distributions

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Find a reputable tutorial on statistical distributions
Follow the tutorial, taking notes on key concepts
Complete any practice exercises or quizzes provided in the tutorial

Engage in a peer coding session

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Gain different perspectives and improve your visualization skills through a collaborative peer session.

Browse courses on Data Visualization

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Find a classmate or colleague to collaborate with
Choose a dataset to visualize together
Discuss different visualization techniques and select the most appropriate ones
Create the visualizations and present them to each other

Develop a machine learning model using a real-world dataset

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Apply your skills by building a machine learning model that addresses a real-world problem.

Browse courses on Machine Learning

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Identify a problem that you would like to solve with machine learning
Gather and pre-process the necessary data
Select and train a machine learning model
Evaluate the performance of your model and make any necessary adjustments
Write up your findings and present them to others

Start a data analysis project for your portfolio

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Gain practical experience and showcase your skills by building a data analysis project.

Browse courses on Data Analysis

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Choose a topic for your project
Gather and prepare your data
Analyze your data and draw insights
Visualize your findings
Write up your results and present them to others

Career center

Learners who complete Statistics and Data Analysis with Excel, Part 1 will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use statistics and probability to collect, clean, and analyze data. They use mathematical and computational methods to interpret data and draw conclusions. This course provides a strong foundation in these concepts, making it a valuable resource for aspiring Data Analysts. Furthermore, the course's focus on data visualization and probability theory can help Data Analysts explore complex and nuanced relationships within data.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians apply mathematical and statistical principles to solve real-world problems. They design and conduct studies, analyze data, and draw conclusions based on their findings. This course provides a solid foundation in statistics, probability, and data analysis, equipping aspiring Statisticians with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory is particularly relevant to Statistical work.

See salaries and explore the career path for Statistician

Market Researcher

Market Researchers gather and analyze data to understand consumer behavior and market trends. They use statistical methods to design surveys, analyze data, and draw conclusions. This course provides a strong foundation in these concepts, making it a valuable resource for aspiring Market Researchers. The course's focus on data visualization and probability theory can help Market Researchers identify and interpret patterns and trends in market data.

See salaries and explore the career path for Market Researcher

Data Scientist

Data Scientists use statistics, machine learning, and data mining techniques to extract insights from data. They build and deploy models to predict outcomes and make data-driven decisions. This course provides a strong foundation in statistics, probability, and data analysis, equipping aspiring Data Scientists with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory is particularly relevant to Data Science work.

See salaries and explore the career path for Data Scientist

Financial Analyst

Financial Analysts use statistical and financial models to evaluate investment opportunities and make recommendations. They analyze financial data, identify trends, and forecast future performance. This course provides a solid foundation in statistics, probability, and data analysis, equipping aspiring Financial Analysts with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory is particularly relevant to Financial Analyst work.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to model financial markets and develop trading strategies. They analyze data, build models, and make predictions about future market behavior. This course provides a strong foundation in statistics, probability, and data analysis, equipping aspiring Quantitative Analysts with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory is particularly relevant to Quantitative Analyst work.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical principles to assess risk and uncertainty. They develop and price insurance policies, evaluate pension plans, and make other financial decisions. This course provides a strong foundation in statistics, probability, and data analysis, equipping aspiring Actuaries with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory is particularly relevant to Actuarial work.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to optimize business processes and improve decision-making. They analyze data, build models, and develop solutions to complex problems. This course provides a solid foundation in statistics, probability, and data analysis, equipping aspiring Operations Research Analysts with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory is particularly relevant to Operations Research Analyst work.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

Business Analysts use data analysis and problem-solving skills to help businesses improve their performance. They analyze data, identify trends, and make recommendations for improvement. This course provides a solid foundation in statistics, probability, and data analysis, equipping aspiring Business Analysts with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory can help Business Analysts identify and interpret patterns and trends in business data.

See salaries and explore the career path for Business Analyst

Risk Manager

Risk Managers identify, assess, and manage risks to organizations. They analyze data, develop risk mitigation strategies, and make recommendations to senior management. This course provides a solid foundation in statistics, probability, and data analysis, equipping aspiring Risk Managers with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory is particularly relevant to Risk Manager work.

See salaries and explore the career path for Risk Manager

Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure. They work with data scientists and other stakeholders to ensure that data is available, reliable, and secure. This course provides a solid foundation in data analysis and probability, equipping aspiring Data Engineers with the necessary skills to succeed in the field. The course's emphasis on data visualization and probability theory can help Data Engineers understand the data they are working with and make informed decisions about data management.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. They use a variety of programming languages and tools to create software that meets the needs of users. This course provides a solid foundation in data analysis and probability, equipping aspiring Software Engineers with the necessary skills to develop data-driven software applications. The course's emphasis on data visualization and probability theory can help Software Engineers understand the data their software is working with and make informed decisions about software design.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to define product requirements, set timelines, and track progress. This course may be useful for aspiring Product Managers as it provides a solid foundation in data analysis and probability. By understanding how to collect, analyze, and interpret data, Product Managers can make better decisions about product development and launch.

See salaries and explore the career path for Product Manager

User Experience Researcher

User Experience Researchers study how users interact with products and services. They conduct research, analyze data, and make recommendations to improve the user experience. This course may be useful for aspiring User Experience Researchers as it provides a solid foundation in data analysis and probability. By understanding how to collect, analyze, and interpret data, User Experience Researchers can make better decisions about how to improve the user experience.

See salaries and explore the career path for User Experience Researcher

Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. They work with a variety of stakeholders to define marketing goals, set budgets, and track results. This course may be useful for aspiring Marketing Managers as it provides a solid foundation in data analysis and probability. By understanding how to collect, analyze, and interpret data, Marketing Managers can make better decisions about marketing campaigns.

See salaries and explore the career path for Marketing Manager