Data Analytics Career Path: 72 Days of Data Analyst Bootcamp from Udemy

Are you eager to embark on a rewarding journey into the world of data analytics? Welcome to the Data Analytics Career Track, where you'll gain a comprehensive skill set and invaluable knowledge to thrive as a data analyst.

Course Overview: Embark on a transformative 72-day journey into the world of data analytics, where you'll learn the essential skills and tools to become a successful data analyst. This comprehensive course is designed to take you from a beginner to a proficient data analyst, equipping you with the knowledge and practical experience needed to excel in the field.

Key Objectives:

Proficiency in Essential Tools: The course curriculum is structured to cover three core pillars of data analysis: Excel, SQL, and Python. You'll start by mastering Excel, the industry-standard spreadsheet software, learning how to manipulate data, perform calculations, and create visualizations to communicate insights effectively.
Hands-on Experience: Engage in practical data analysis projects and coding exercises, honing your problem-solving skills through immersive learning experiences. With a focus on hands-on learning, you'll work on real-world projects and case studies, applying your newfound skills to solve practical challenges faced by data analysts in various industries.
Foundational Knowledge: Gain insights into data analysis theories, statistical methods, hypothesis testing, and machine learning fundamentals, laying a solid groundwork for your career. Learn A-Z data cleaning and manipulation techniques, including sorting, filtering, conditional formatting, and advanced analysis with pivot tables and charts. Acquire a deep understanding of relational database management systems (RDBMS), covering key concepts such as primary keys, foreign keys, and SQL manipulation.
Excel Proficiency: You'll start by mastering Excel, the industry-standard spreadsheet software, learning how to manipulate data, perform calculations, and create visualizations to communicate insights effectively.
SQL Proficiency: You'll dive into SQL, the language of databases, gaining proficiency in querying and manipulating data stored in relational databases. You'll learn how to extract relevant information using SQL commands, perform data joins and aggregations, and optimize queries for efficiency.
Python Proficiency: you'll explore Python, a powerful programming language widely used for data analysis and visualization. You'll discover how to leverage Python libraries such as Pandas, NumPy, and Matplotlib to conduct advanced data analysis, automate tasks, and create interactive visualizations.
ChatGPT Proficiency: you'll use ChatGPT for data preparation, including dealing with missing data, outliers, and converting data types, complex data manipulation tasks, such as merging datasets, creating pivot tables, executing sophisticated data analysis, identifying trends, patterns, and making predictions using advanced machine learning models like the random forest regressor.
Power BI Proficiency: You'll become proficient in Power BI, a leading business analytics tool that allows you to connect to various data sources, transform raw data into meaningful insights, and create interactive dashboards and reports. You'll learn how to utilize Power Query for data cleaning and transformation, design visually appealing and informative charts and dashboard.
Practical Assignments: Challenge yourself with over 50 practical assignments, 140 coding exercises, and 10 quizzes spanning the breadth of the course curriculum.
Capstone Projects: Apply your newfound skills to real-world scenarios with two comprehensive capstone projects focused on bank data analysis and sports data analysis, providing a holistic view of the data analytics workflow.

Benefits of the Course:

Career Readiness: Prepare for a successful career as a data analyst with essential professional skills and practical knowledge.
Versatility: Gain proficiency in multiple tools and techniques, making you adaptable to diverse data analysis scenarios and industry demands.
Problem-solving Skills: Enhance your analytical and critical thinking abilities through hands-on data analysis exercises and coding challenges.
Industry-Relevant Learning: Stay ahead of the curve with up-to-date insights into data analysis methodologies and best practices.
Portfolio Enhancement: Build a robust portfolio showcasing your expertise through practical projects and assignments, demonstrating your readiness for the job market.

Join us on the Data Analytics Career Track and unlock endless possibilities in the world of data analysis. Whether you're looking to kickstart a career in data analytics or enhance your existing skills, this course will empower you to succeed in the dynamic world of data. Join us on this exciting journey and unlock your potential as a data analyst in just 60 days.

What's inside

Learning objectives

Gain proficiency in excel, sql, python, power bi and chatgpt to prepare for a career as a data analyst with essential skills and knowledge.
Learn facts and theories for data analysis, statistical analysis, hypothesis testing, and machine learning for foundations of data analytics.
Learn excel for data cleaning and manipulation methods, sorting and conditional filtering, formulas, and functions, graphs and charts.
Learn excel for advanced analysis in pivot tables and charts, data analysis toolpak for statistical analysis and interactive dashboard.
Learn rdbms fundamentals, covering key concepts such as primary and foreign keys, data types, and the various types of rdbms and more.
Learn mysql for manipulation of tables, columns, constraints, indices, null values, filtering, joining methods.
Learn the important python basics such as variables, data types, lists, dictionaries, dataframes, sets, loops, functions etc.
Master python for data cleaning, data manipulation, data analysis, transformation, and data preprocessing.
Learn to use python for data visualizations, exploratory data analysis, statistical analysis, and machine learning models.

Utilize chatgpt for advanced data manipulation tasks, such as merging datasets, creating pivot tables, and applying conditional logic.
Learn to apply chatgpt for predictive analytics, including random forest regressor and other machine learning models.
Learn power bi for data manipulation, analysis and extract significant insight and prepare for attractive dashboard.
Use power bi to create a professional, informative and amazing dashboard that tells story about your data and provide insights.
Overcome 50+ practical assignments, 140+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire career track.
Accomplish three capstone projects: bank churn analysis, sports data analytics and website performance analysis.
Show more
Show less

Gain proficiency in excel, sql, python, power bi and chatgpt to prepare for a career as a data analyst with essential skills and knowledge.
Learn facts and theories for data analysis, statistical analysis, hypothesis testing, and machine learning for foundations of data analytics.
Learn excel for data cleaning and manipulation methods, sorting and conditional filtering, formulas, and functions, graphs and charts.
Learn excel for advanced analysis in pivot tables and charts, data analysis toolpak for statistical analysis and interactive dashboard.
Learn rdbms fundamentals, covering key concepts such as primary and foreign keys, data types, and the various types of rdbms and more.
Learn mysql for manipulation of tables, columns, constraints, indices, null values, filtering, joining methods.
Learn the important python basics such as variables, data types, lists, dictionaries, dataframes, sets, loops, functions etc.
Master python for data cleaning, data manipulation, data analysis, transformation, and data preprocessing.
Learn to use python for data visualizations, exploratory data analysis, statistical analysis, and machine learning models.
Utilize chatgpt for advanced data manipulation tasks, such as merging datasets, creating pivot tables, and applying conditional logic.
Learn to apply chatgpt for predictive analytics, including random forest regressor and other machine learning models.
Learn power bi for data manipulation, analysis and extract significant insight and prepare for attractive dashboard.
Use power bi to create a professional, informative and amazing dashboard that tells story about your data and provide insights.
Overcome 50+ practical assignments, 140+ coding exercises, 10 quizzes with 100+ questions, on all the topics over the entire career track.
Accomplish three capstone projects: bank churn analysis, sports data analytics and website performance analysis.
Show more
Show less

Syllabus

Phase 1 - Data Analytics Fundamentals

My instructions for this phase

Get my special Guidebook

Extra note on analytical world of data

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Excel, SQL, and Python, which are foundational tools for data analysis roles across various industries

Includes over 50 practical assignments and 140 coding exercises, providing ample opportunity for hands-on practice and skill development

Incorporates ChatGPT for data preparation and predictive analytics, reflecting current trends in data analysis and machine learning

Features capstone projects focused on bank and sports data, offering practical experience with real-world data analysis scenarios

Requires learners to become proficient in Power BI, a leading business analytics tool used for creating interactive dashboards and reports

Spends a significant amount of time on Excel, which may be too basic for learners with prior experience in data analysis or spreadsheet software

Reviews summary

Comprehensive data analytics career path

According to learners, the Data Analytics Career Path course offers comprehensive coverage of essential tools like Excel, SQL, Python, Power BI, and ChatGPT, aiming to prepare students for a data analyst career. Students appreciate the emphasis on hands-on projects and practical assignments which help solidify understanding. However, some learners note the fast pace and feel that certain topics could benefit from more depth. It is often recommended for beginners looking for a structured introduction across multiple technologies.

Ideal for those new to data analytics.

"This is a great starting point if you know nothing about data analytics coming in."

"If you have some background in SQL or Python, parts might feel too basic for you."

"It's perfect for someone looking to make a career switch into data and needs a broad introduction."

"Provides a solid foundation for absolute beginners."

Valuable hands-on experience provided.

"The capstone projects were the highlight; they really helped solidify my understanding by applying everything."

"There are plenty of coding exercises included, which is great for practice."

"I appreciate the practical assignments included throughout the course modules."

"Working on real-world type projects made the concepts stick better."

Covers a wide range of essential data skills.

"I learned Excel, SQL, and Python all in one place, which is great."

"The course covers all the core tools you need for a data analyst role."

"The syllabus is incredibly detailed and covers both theory and practical application."

"It introduces you to Excel, SQL, Python, Power BI, and even ChatGPT for data tasks."

Some assignments could be clearer.

"Some of the coding exercises were a bit confusing to understand initially."

"I sometimes needed to look elsewhere or ask in forums for clarification on assignments."

"The difficulty of the assignments didn't always seem to match the preceding lecture."

"Instructions could be more precise for certain tasks."

Moves quickly; some topics lack depth.

"It covers a lot of ground, but honestly, it moves really fast in some sections."

"I wish they spent more time going deeper into the Python libraries for analysis."

"Trying to get through everything covered in just 72 days felt a bit rushed at times."

"Some modules felt a bit superficial, especially the more advanced ones."

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 Career Path: 72 Days of Data Analyst Bootcamp with these activities:

Review Statistical Concepts

Show steps

Solidify your understanding of statistical concepts like hypothesis testing, p-values, and regression analysis to better grasp the statistical methods used in data analysis.

Browse courses on Statistical Data Analysis

Show steps

Review introductory statistics textbooks or online resources.
Work through practice problems related to hypothesis testing and regression.
Summarize key statistical concepts and formulas.

Review 'Python for Data Analysis'

Show steps

Deepen your understanding of Python for data analysis by studying this comprehensive guide, focusing on Pandas and NumPy libraries for data wrangling and analysis.

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

Show steps

Read the book and practice the code examples.
Experiment with different data analysis techniques using Pandas and NumPy.
Apply the concepts learned to solve real-world data analysis problems.

Review 'Storytelling with Data'

Show steps

Learn how to effectively communicate data insights through compelling visualizations and narratives, enhancing your ability to present findings in Power BI.

View Storytelling with You: Plan, Create, and... on Amazon

Show steps

Read the book and take notes on key concepts.
Identify examples of effective and ineffective data visualizations.
Apply the principles learned to improve your own data presentations.

Four other activities

Expand to see all activities and additional details

Show all seven activities

SQL Query Practice

Show steps

Sharpen your SQL skills by practicing various query types, including joins, aggregations, and subqueries, to improve your ability to extract and manipulate data from databases.

Show steps

Set up a local database with sample data.
Work through SQL exercises on platforms like LeetCode or HackerRank.
Write queries to solve specific data analysis problems.

Personal Finance Tracker

Show steps

Apply your Excel and data analysis skills to create a personal finance tracker that helps you monitor your income, expenses, and savings.

Show steps

Gather your financial data from bank statements and other sources.
Design an Excel spreadsheet to track your income and expenses.
Use formulas and functions to calculate key metrics like savings rate and net worth.
Create charts and graphs to visualize your financial performance.

Create a Data Analysis Blog Post

Show steps

Solidify your understanding of a specific data analysis technique or tool by writing a blog post explaining its concepts, applications, and best practices.

Show steps

Choose a data analysis topic covered in the course.
Research the topic and gather relevant information.
Write a clear and concise blog post explaining the topic.
Include examples and visuals to illustrate key concepts.

Build a Power BI Dashboard

Show steps

Create an interactive Power BI dashboard to visualize and analyze a dataset of your choice, showcasing your ability to connect to data sources, transform data, and create compelling visualizations.

Show steps

Choose a dataset to analyze (e.g., public health data, sales data).
Connect Power BI to the data source.
Clean and transform the data using Power Query.
Create visualizations to explore the data and identify insights.
Design an interactive dashboard to present your findings.

Career center

Learners who complete Data Analytics Career Path: 72 Days of Data Analyst Bootcamp will develop knowledge and skills that may be useful to these careers:

Data Analyst

A data analyst uses tools to interpret data and provide insights. The Data Analytics Career Path course directly prepares learners for this role. Through instruction in Excel, SQL, and Python, the course helps build a foundation in data manipulation, analysis, and presentation. The hands-on experience with real-world projects, combined with instruction in Power BI, will give learners the tools they need to succeed as a data analyst.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business intelligence analysts use data to understand market trends, improve business efficiency, and support decision-making. This course helps learners gain proficiency in data analysis via Excel, SQL, and Python, which are vital for this role. The course also teaches Power BI and its capacity to create dashboards and reports. The focus on hands-on experience and real-world projects in this course can prepare learners for a career as a business intelligence analyst.

See salaries and explore the career path for Business Intelligence Analyst

Market Research Analyst

Market research analysts study consumer behavior and market trends to advise companies on product development and marketing strategies. The Data Analytics Career Path course is helpful for this role because it provides a strong foundation in data manipulation and analysis with tools like Excel, SQL, and Python. Additionally, the course's coverage of data visualization using Power BI helps market research analysts communicate findings and trends effectively. The capstone projects provide practical experience.

See salaries and explore the career path for Market Research Analyst

Financial Analyst

Financial analysts examine financial data to provide guidance on investment decisions and financial planning. The Data Analytics Career Path course may help learners succeed in this role. The course's emphasis on data analysis, coupled with instruction in Excel, SQL, and Python, provides learners with a solid grounding in relevant data manipulation techniques. The course can be especially useful for those looking to use data to inform financial decisions and present findings through visualizations similar to what's done in Power BI.

See salaries and explore the career path for Financial Analyst

Operations Analyst

Operations analysts study data to improve a company's efficiency and performance. The Data Analytics Career Path course can be useful for this role by providing learners with fundamental skills in data analysis and manipulation. The course's emphasis on Excel, SQL, and Python, complemented by practical projects and assignments, can help learners develop the skills needed to succeed as an operations analyst. Furthermore, the course's instruction in Power BI will prove useful for the role's reporting duties.

See salaries and explore the career path for Operations Analyst

Data Engineer

Data engineers build the systems that enable data analysis, which includes tasks such as designing and maintaining databases. While this course is more focused on data analysis, its curriculum in SQL provides a good introduction to database management systems. The course will familiarize learners with foundational concepts such as primary and foreign keys, along with data manipulation using SQL. This foundational exposure may be helpful for those considering a transition into the field of data engineering.

See salaries and explore the career path for Data Engineer

Database Administrator

Database administrators are responsible for the management, security, and performance of databases. The Data Analytics Career Path course's SQL training may be helpful for those interested in this role. The course provides an introduction to relational database management systems and data manipulation using SQL. Though this course's focuses primarily on data analysis, its SQL curriculum provides relevant knowledge for database management and administration.

See salaries and explore the career path for Database Administrator

Statistical Analyst

Statistical analysts apply statistical methods to analyze data and generate insights. The Data Analytics Career Path course may be helpful to perform this role due to its coverage of statistical methods and hypothesis testing. The course introduces statistical concepts and also provides hands-on training with tools like Python, which are often used to conduct statistical analysis. The course's practical projects help the learner apply this knowledge. This course will help build a beginner's knowledge of statistical analysis.

See salaries and explore the career path for Statistical Analyst

Machine Learning Engineer

Machine learning engineers design and build machine learning systems. While this course is not focused on machine learning as a primary skill, it may be helpful by introducing machine learning concepts in its curriculum. The Data Analytics Career Path course introduces machine learning fundamentals. Learners will also gain experience using Python for predictive analytics, including the random forest regressor. This may be helpful to those interested in an adjacent career in machine learning.

See salaries and explore the career path for Machine Learning Engineer

Research Associate

Research associates perform experiments and analyze data, often within a specific field of study. The Data Analytics Career Path course may be useful for this role. The course provides instruction in data analysis techniques using Excel, SQL, and Python. The course's emphasis on data cleaning and manipulation, statistical analysis, and hypothesis testing may be useful for the research process. The course may be helpful as a first step toward a career in research.

See salaries and explore the career path for Research Associate

Project Manager

Project managers oversee the planning and execution of projects, ensuring they are completed on time and within budget. While the Data Analytics Career Path course focuses on data analysis, the course may be a useful tool for project administration and reporting. The course's lessons with Power BI and its reporting capabilities may be particularly relevant for this role. Furthermore, the course's capstone projects provide practical experience with workflows that may be relevant to task management and planning.

See salaries and explore the career path for Project Manager

Management Consultant

Management consultants advise organizations on how to improve their performance and efficiency. While the Data Analytics Career Path course doesn't include comprehensive business strategy training, the course's emphasis on data analysis and insights may be useful to this role. The course may help a learner better understand data, and it provides a useful skillset in extracting relevant information through tools like SQL and Power BI. This course may introduce a path to consulting in data.

See salaries and explore the career path for Management Consultant

Technical Writer

Technical writers create documentation for technical products and services. This career may be a useful way for learners to use the skills they gained from the Data Analytics Career Path. The course offers a foundation in many technologies including SQL, Python, and PowerBI, tools often discussed in technical documentation. Though not directly related to technical writing, a learner of this course may use the material learned as a basis for technical documentation projects. The course may help a technical writer in a tech-heavy position.

See salaries and explore the career path for Technical Writer

Instructor

An instructor educates, trains, and transfers knowledge to learners. This career option may be useful to a learner of the Data Analytics Career Path, especially for those wishing to teach data analytics. This course provides a comprehensive survey of data analysis tools and techniques. The hands-on projects will teach a learner the practical and pedagogical aspects of this subject. The course may be helpful prep for teaching or tutoring in data analysis.

See salaries and explore the career path for Instructor

Research Scientist

Research scientists conduct research to advance knowledge in their given field. An advanced degree is often required for this role. While the Data Analytics Career Path course is not intended to be a comprehensive guide to research, it may provide helpful initial skills with its lessons in hypothesis testing and statistical methods. Moreover, the course's data analysis with Python could help with the research process. This course may be helpful as a basic intro to research methods.

See salaries and explore the career path for Research Scientist

Data Analytics Career Path

72 Days of Data Analyst Bootcamp

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Comprehensive data analytics career path

Activities

Career center

Reading list

Share

Similar courses