Data Analytics Engineering: Probability & Techniques from Coursera

What's inside

Syllabus

Introduction to Python

In this module, we will focus on Python programming fundamentals. The aim is to help understand Python's basic syntax, data types, and operators, enabling the creation of simple programs. Additionally, we will cover the use of if statements, loops, and proper indentation to control program flow, fostering a foundational understanding of essential control structures in Python programming.

Data Structures

In this module, we will dive into the diverse landscape of Python data structures, including lists, dictionaries, sets, tuples, and arrays. By exploring real-world use cases, you will uncover the unique strengths and weaknesses of each data structure. You will gain insights into recognizing and understanding the characteristics of these structures, empowering you to make informed choices when tackling programming challenges. Through hands-on practice, you will develop the skills to select and apply the most suitable data structure to efficiently solve a wide range of problems, enhancing your proficiency in Python programming.

Modern Data Structures and Data Wrangling

In this module we will introduce DataFrames, a pivotal tool in data manipulation and analysis. You will grasp the fundamental concepts of DataFrames, learning how to create, manipulate, and access data efficiently. You will gain essential skills for basic data exploration–including summarizing data, indexing, and slicing, enabling them to extract meaningful insights. Furthermore, this module equips learners with the expertise to clean and preprocess data, covering handling missing values, filtering data, merging/joining datasets, and transforming data for analysis readiness. By the end of this module, you will harness DataFrames for advanced data analysis, mastering group-wise operations, aggregation, and statistical analysis.

Exploratory Data Analysis

This module will equip you with a comprehensive toolkit for proficient data exploration and analysis. It covers the essential techniques and tools for effectively summarizing data sets, encompassing statistical summaries, data visualization, and data cleaning methods. You will learn how to identify and assess missing data, outliers, and anomalies, vital tasks during the initial exploratory phase of data analysis. Furthermore, you will develop the ability to uncover patterns, relationships, and trends within the data using various visualizations, including scatter plots, histograms, and correlation matrices, enabling them to extract valuable insights and make informed decisions from their data.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces data structures, which are essential for organizing and managing data in programming

Features instructors from Sri Radhakrishnan, who have expertise in the field

Develops foundational programming skills in Python, which are widely used in industry

Emphasizes data cleaning and data wrangling techniques, which are crucial for handling real-world datasets

Provides practical applications of probability and distribution, cluster analysis, text analysis, and time series analysis, which are valuable for data science and machine learning

Requires students to come in with some programming background, which may be a barrier for complete beginners

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 Data Analytics Engineering: Probability & Techniques with these activities:

Review Python basics and data structures

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Strengthen your foundation by reviewing Python basics and data structures, ensuring a solid understanding of core concepts before starting the course.

Browse courses on Python Syntax

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Go through Python tutorials or reference materials
Practice writing simple Python code snippets

Read 'Python for Data Analysis' by Wes McKinney

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Expand your knowledge by reading a comprehensive book that covers Python for data analysis concepts and techniques, providing a deeper understanding of the course material.

View Python for Data Analysis: Data Wrangling with... on Amazon

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Find a quiet place to read and focus
Break down the chapters into manageable sections
Take notes or highlight important concepts

Python for Data Science Tutorial

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Build a stronger foundation in Python programming by following tutorials. This can be especially helpful for students who have little to no experience with Python, ensuring they are better prepared for the course materials.

Browse courses on Python

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Identify resources for Python tutorials
Start with beginner-friendly tutorials
Practice writing Python code
Complete coding challenges

Six other activities

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Coding Challenges in Python

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Enhance proficiency in Python programming through dedicated practice. Engaging in coding challenges will help students strengthen their understanding of data structures and the Pandas library.

Browse courses on Python

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Find online coding platforms
Start with easier challenges
Progress to more complex problems

Follow along with Python tutorials

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Supplement your understanding by following along with step-by-step tutorials to explore specific Python concepts and techniques.

Browse courses on Python Basics

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Identify topics or areas where you need additional guidance
Find high-quality tutorials from reputable sources
Follow the instructions and complete the exercises

Exploratory Data Analysis Project

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Apply course concepts in a practical setting. Undertaking an exploratory data analysis project will allow students to gain hands-on experience in data exploration, visualization, and interpretation, reinforcing their understanding of these techniques.

Browse courses on Exploratory Data Analysis

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Choose a dataset
Explore and clean the data
Conduct exploratory analysis
Create data visualizations
Write a report on findings

Solve Python coding problems

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Practice solving coding problems in Python to reinforce your understanding of Python syntax and programming concepts.

Browse courses on Python Programming

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Choose a coding platform or resource
Select a coding problem of appropriate difficulty
Attempt to solve the problem on your own
Review solutions and explanations

Develop a Python script or project

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Apply your Python skills by creating a practical script or project that demonstrates your understanding of data structures, data analysis, and visualization techniques.

Browse courses on Python

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Identify a problem or task that can be solved with Python
Design and plan the script or project
Implement your solution using Python
Test and refine your script or project

Contribute to Python open-source projects

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Gain hands-on experience by contributing to Python open-source projects, allowing you to apply your skills, learn from others, and give back to the community.

Browse courses on Data Science Tools

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Identify open-source projects related to Python and data science
Understand the project's goals and contribution guidelines
Make code contributions, bug fixes, or documentation improvements

Career center

Learners who complete Data Analytics Engineering: Probability & Techniques will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use scientific methods and processes to extract knowledge and insights from data. The course covers essential topics for Data Scientists, such as probability, cluster analysis, and text analysis. This course could be very useful for Data Scientists.

See salaries and explore the career path for Data Scientist

Actuary

Actuaries use mathematical and statistical models to assess risk in the insurance and finance industries. This course covers the fundamentals of probability and statistics, which are essential for Actuaries. This course could be very useful for them.

See salaries and explore the career path for Actuary

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and drive investment decisions. This course could be useful in this profession, as it provides a foundation in probability, statistics, and programming.

See salaries and explore the career path for Quantitative Analyst

Data Visualization Specialist

Data Visualization Specialists use visual representations to communicate data insights. This course could be useful for them, as it covers the fundamentals of data analysis, data wrangling, and data visualization.

See salaries and explore the career path for Data Visualization Specialist

Data Engineer

Data Engineers design, construct, deploy, and manage data pipelines and databases. This course could be very useful for this role, as it covers a variety of topics important to the profession, including data structures, data wrangling, probability, and exploratory data analysis.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning systems. This course could be very useful for these professionals, as it covers probability, text analysis, and time series analysis. These skills will be helpful for aspiring Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Market Researcher

Market Researchers conduct research on consumer behavior to help businesses understand and target their audience. This course could be useful for someone in this profession, as it covers data wrangling, exploratory data analysis, and text analysis.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

Operations Research Analysts apply analytical methods to help organizations make better decisions. This course could be useful in this line of work, as it covers probability, optimization, and simulation, which are all skills commonly used by Operations Research Analysts.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians collect, analyze, interpret, and present data. This course could be very useful for them since it covers conceptual and practical applications of probability and distribution, which are core skills for Statisticians.

See salaries and explore the career path for Statistician

Risk Analyst

Risk Analysts assess and manage financial risks. This course could be useful for Risk Analysts, as it covers probability, statistics, and modeling.

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Data Analyst

Data Analysts sift through immense amounts of structured data to find trends and patterns that assist other professionals with decision-making. This course could be helpful in this line of work, as it discusses the conceptual and practical applications of probability, as well as cluster, text, and time series analyses, all of which are important skills for Data Analysts. These skills will help build a foundation for success in this field.

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Financial Analyst

Financial Analysts evaluate and interpret financial data to make recommendations for investment. This course may be useful, as it covers probability and distribution, which are important for understanding financial trends.

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Software Engineer

Software Engineers apply engineering principles to software development. This course could be useful for Software Engineers, as it covers the fundamentals of computation, probability, and distribution, as well as modern data structures and data wrangling, all while using Python. This course may help someone become a more proficient Software Engineer.

See salaries and explore the career path for Software Engineer

Business Analyst

Business Analysts leverage data to understand business problems and opportunities. This course could be useful, as it covers important topics including probability, data structures, and exploratory data analysis. This course may help one become a more proficient Business Analyst.

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Database Administrator

Database Administrators set up, configure, and maintain databases. They must have a deep understanding of data structures to work with ease, and this course covers many relevant data structures, such as lists, dictionaries, sets, tuples, and arrays. This course may be useful in this line of work.

See salaries and explore the career path for Database Administrator