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

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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