Probability Theory, Statistics and Exploratory Data Analysis from Coursera

Exploration of Data Science requires certain background in probability and statistics. This online course introduces you to the necessary sections of probability theory and statistics, guiding you from the very basics all way up to the level required for jump starting your ascent in Data Science. The core concept of the course is random variable â€” i.e. variable whose values are determined by random experiment. Random variables are used as a model for data generation processes we want to study. Properties of the data are deeply linked to the corresponding properties of random variables, such as expected value, variance and correlations. Dependencies between random variables are crucial factor that allows us to predict unknown quantities based on known values, which forms the basis of supervised machine learning. We begin with the notion of independent events and conditional probability, then introduce two main classes of random variables: discrete and continuous and study their properties. Finally, we learn different types of data and their connection with random variables. While introducing you to the theory, we'll pay special attention to practical aspects for working with probabilities, sampling, data analysis, and data visualization in Python. This course requires basic knowledge in Discrete mathematics (combinatorics) and calculus (derivatives, integrals). This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.

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Explores probability theory and statistics, crucial for Data Science

Provides a solid foundation in probability and statistics for aspiring Data Scientists

Introduces random variables and their properties, essential for data analysis

Connects random variables with data types, enabling practical applications

Emphasizes hands-on aspects of probability in Python, enhancing practical skills

Requires basic knowledge in Discrete mathematics and calculus, setting clear prerequisites

Reviews summary

Probability and statistics for data science

This course provides a comprehensive introduction to probability theory and statistics for beginners in data science. It introduces the fundamentals of probability theory, including random variables, distributions, and Bayes' theorem. The course also covers descriptive statistics, hypothesis testing, and linear regression, providing students with a solid foundation in statistical methods. The course is taught by an experienced instructor who provides clear explanations and engaging examples, making it accessible to students with varying levels of mathematical background.

Focuses on practical aspects of probability and statistics for data science.

"We'll pay special attention to practical aspects for working with probabilities, sampling, data analysis, and data visualization in Python."

Includes practical examples and exercises in Python for data analysis.

"Excellent course. To the point with no fluff. The professor explained everything in just the right amount of detail and the inclusion of python is great too."

Suitable for beginners with no prior knowledge of probability or statistics.

"Exploration of Data Science requires certain background in probability and statistics. This online course introduces you to the necessary sections of probability theory and statistics, guiding you from the very basics all way up to the level required for jump starting your ascent in Data Science."

Interactive quizzes and assignments help students engage with the material and assess their understanding.

"Absolutely enjoyable statistics class. Lots of quizzes that keep you engaged and help get a deeper understanding of the materials presented. Best!"

Clear explanations and engaging examples make concepts easy to understand.

"The professor explains very well."

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 Probability Theory, Statistics and Exploratory Data Analysis with these activities:

Review Probability Concepts

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Review the core concepts of probability theory, with a particular focus on random variables and their properties.

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Re-familiarize yourself with the basic concepts of probability, including events, sample spaces, and probability distributions.
Focus on understanding the concept of random variables and their different types (discrete vs continuous).
Practice calculating probabilities involving random variables, such as expected value, variance, and covariance.

Work through Probability Practice Problems

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Solve a variety of practice problems related to probability theory, focusing on types of random variables, probability distributions, and statistical inference.

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Find practice problems with solutions or access online resources that provide interactive exercises.
Work through the problems, focusing on understanding the concepts and applying the formulas.
Check your answers against the solutions to identify areas for improvement.

Explore Python Libraries for Data Analysis

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Learn how to use Python libraries, such as NumPy, Pandas, and Matplotlib, to import, manipulate, and visualize data. Create visualizations such as histograms, scatterplots, and box plots to gain insights from the data.

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Follow guided tutorials that introduce the basics of NumPy, Pandas, and Matplotlib.
Practice using the libraries to load, clean, and analyze sample datasets.
Create visualizations to explore the data and identify patterns and trends.

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Explore Applications of Probability in Machine Learning

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Investigate the role of probability theory in machine learning algorithms, such as Naive Bayes, linear regression, and logistic regression. Understand how probability distributions are used to model data and make predictions.

Browse courses on Machine Learning

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Review the basics of machine learning concepts, such as supervised learning, unsupervised learning, and model evaluation.
Study how probability distributions are used to represent data in machine learning algorithms.
Explore specific machine learning algorithms that utilize probability theory, such as Naive Bayes, regression, and logistic regression.

Develop a Simulation Model for a Random Experiment

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Build a simulation model in Python to represent a real-world random experiment. Use the model to generate data and perform statistical analysis to test hypotheses or estimate probabilities.

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Define the random experiment and identify the relevant random variables and distributions.
Implement the simulation model using Python, taking into account the distributions and relationships between variables.
Run the simulation to generate data and perform statistical analysis on the results.
Write a report summarizing the simulation results and the insights gained.

Participate in Data Science Competitions

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Engage in data science competitions on platforms like Kaggle or DrivenData. Gain practical experience in applying your skills and knowledge to solve real-world data problems.

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Identify a competition aligned with your interests and skill level.
Download the dataset and familiarize yourself with the problem statement.
Develop and implement a solution using appropriate machine learning algorithms and techniques.
Submit your solution and track your progress on the leaderboard.

Career center

Learners who complete Probability Theory, Statistics and Exploratory Data Analysis will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians collect, analyze, interpret, and present data. They use their expertise in probability, statistics, and data analysis to solve problems in a wide variety of fields, such as medicine, finance, and marketing. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Statistician, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists are responsible for developing and implementing data-driven solutions to business problems. They use their expertise in statistics, machine learning, and data analysis to extract insights from data and build predictive models. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Data Scientist, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

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Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They use their expertise in statistics, computer science, and machine learning to build models that can learn from data and make predictions. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Machine Learning Engineer, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

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Data Visualization Specialist

Data Visualization Specialists use data visualization tools and techniques to communicate data insights to a variety of audiences. They use their expertise in data analysis, visual design, and communication to create visualizations that are both informative and engaging. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Data Visualization Specialist, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

See salaries and explore the career path for Data Visualization Specialist

Data Analyst

Data Analysts help businesses understand their data through analysis and modeling. They use various statistical techniques and machine learning algorithms to identify trends and patterns in data, which can help businesses make better decisions. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Data Analyst, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use their expertise in probability, statistics, and finance to develop trading strategies and risk management models. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Quantitative Analyst, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

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Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. They use their expertise in probability, statistics, and finance to develop insurance policies and pension plans. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as an Actuary, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency of business operations. They use their expertise in probability, statistics, and optimization to solve problems in a wide variety of industries, such as manufacturing, logistics, and healthcare. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as an Operations Research Analyst, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use financial data and models to make investment recommendations. They use their expertise in finance, accounting, and statistics to analyze companies and make recommendations on whether to buy, sell, or hold stocks. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Financial Analyst, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers collect, analyze, and interpret data about consumer behavior. They use their expertise in marketing, statistics, and data analysis to help businesses understand their customers and make better marketing decisions. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Market Researcher, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

See salaries and explore the career path for Market Researcher

Risk Manager

Risk Managers identify, assess, and manage risks. They use their expertise in probability, statistics, and finance to develop risk management strategies and policies. This course in Probability Theory, Statistics and Exploratory Data Analysis would be a valuable foundation for a career as a Risk Manager, as it provides a solid understanding of the fundamental principles of probability and statistics, as well as practical experience with data analysis and visualization techniques.

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Business Analyst

Business Analysts analyze business processes and systems to identify areas for improvement. They use their expertise in business analysis, statistics, and data analysis to develop recommendations for how to improve business performance. This course in Probability Theory, Statistics and Exploratory Data Analysis may be useful for a career as a Business Analyst, as it provides a solid foundation in the principles of data analysis and management.

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Database Administrator

Database Administrators design, implement, and maintain databases. They use their expertise in database management systems and data analysis to ensure that data is accurate, secure, and accessible. This course in Probability Theory, Statistics and Exploratory Data Analysis may be useful for a career as a Database Administrator, as it provides a solid foundation in the principles of data analysis and management.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use their expertise in computer science and software engineering to create software that is reliable, efficient, and secure. This course in Probability Theory, Statistics and Exploratory Data Analysis may be useful for a career as a Software Engineer, as it provides a solid foundation in the principles of data analysis and management.

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Computer Scientist

Computer Scientists research and develop new computer technologies. They use their expertise in computer science and mathematics to create new algorithms, software, and hardware. This course in Probability Theory, Statistics and Exploratory Data Analysis may be useful for a career as a Computer Scientist, as it provides a solid foundation in the principles of data analysis and management.

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Probability Theory, Statistics and Exploratory Data Analysis

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Probability and statistics for data science

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