Enrolling in the IBM data analyst professional certificate course and gaining proficiency in data analysis skills is paramount in the modern data-driven landscape. This comprehensive course equips individuals with the tools to effectively collect, analyze, and interpret data, enabling informed decision-making and insight extraction. Possessing these skills opens doors to lucrative career opportunities. It empowers industries across the board to leverage data for strategic growth and innovation. We will provide you with a comprehensive review of this professional certificate offered by IBM through Coursera.
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Course Overview: IBM Data Analyst
Start your journey in the high-growth field of data analytics with this program, designed to equip you with in-demand skills like Python, Excel, and SQL to get job-ready in as little as 4 months. This course is tailored for beginners, requiring no prior experience or degree. From entry-level data analytics roles to future career developments like data science and artificial intelligence, this beginner-friendly program provides essential expertise and practical experience through real-world projects, giving you the tools to gather, model, and analyze data that informs executive decision-making. You will learn from subject-matter experts at IBM. After completing the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise.
Course Content:
This professional certificate has a series of 9 well-designed courses which will cover all foundations & advanced topics of data analysis. Each course has modules based on different topics, and each Module is considered complete in one week.
Course 1: Introduction to Data Analytics
This course gives you a gentle introduction to Data Analysis, the role of a data analyst, and the tools used in this job. This course will help you to differentiate between the roles of data analysts, data scientists, and data engineers. You will familiarize yourself with the data ecosystem alongside Databases, Data Warehouses, Data Marts, Data Lakes, and Data Pipelines. Continue this exciting journey and discover Big Data platforms like Hadoop, Hive, and Spark. The estimated course completion time is 10 hours.
Module 1: What is Data Analytics
Module 2: The Data Ecosystem
Module 3: Gathering and Wrangling Data
Module 4: Mining & Visualizing Data and communicating results
Module 5: Career Opportunities and Data Analysis in Action
Course 2: Excel Basics for Data Analysis
This course introduces Excel’s importance for data tasks in various fields, catering to beginners. No coding/spreadsheet experience is needed, only a web browser and Microsoft account. Covers data loading, cleaning, filtering, sorting, pivot tables, and practical learning with hands-on labs. Equips learners to clean, analyze data, and create pivot tables efficiently. The course ends with a project using real data sets, fostering data analysis skills without coding. Get ready to master Excel for data analysis. The estimated course completion time is 11 hours.
Module 1: Introduction to Data Analysis Using Spreadsheets
Module 2: Getting Started with Using Excel Spreadsheets
Module 3: Cleaning & Wrangling Data Using Spreadsheets
Module 4: Analyzing Data Using Spreadsheets
Module 5: Final Project
Course 3: Data Visualizations and Dashboards with Spreadsheets and Cognos
Explore foundational data visualization concepts in this accessible course. Learn to translate data into compelling visuals using various tools. Cover basic to advanced charts and graphs with hands-on practice. No prior coding is required. Gain skills for effective communication through data visuals. Perfect for enhancing analytical and presentation capabilities. The estimated course completion time is 9 hours.
Module 1: Visualizing Data Using Spreadsheet
Module 2: Creating Visualizations and Dashboards with Spreadsheets
Module 3: Creating Visualizations and Dashboards with Cognos Analytics
Module 4: Final Project
Course 4: Python for Data Science, AI & Development
Start your Python journey with an expert-led beginner course. No coding background is needed. Learn Python basics, data structures, libraries like Pandas, and web scraping. Hands-on labs using Jupyter Notebooks. Ideal for Data Science, Analytics, Software Dev, and more roles. The estimated course completion time is 23 hours.
Module 1: Python Basics
Module 2: Python Data structures
Module 3: Python Programming Fundamentals
Module 4: Working with Data in Python
Module 5: APIs and Data Collection
Course 5: Python Project for Data Science
Demonstrate foundational Python skills through this mini-course. Role-play as a Data Scientist or Analyst, working on real-world data and scenarios. Complete tasks like data extraction, web scraping, and visualization using libraries like Pandas and Beautiful Soup within a Jupyter Notebook. Ideal for showcasing Python proficiency in your portfolio. Prerequisite: Completing IBM’s “Python for Data Science, AI, and Development” course or equivalent Python data skills. Apply prior Python knowledge in practical projects. The estimated course completion time is 8 hours.
Module 1: Crowdsourcing Short Squeeze Dashboard
Course 6: Databases and SQL for Data Science with Python
In this course, you will learn SQL from the basics of Select statements to advanced concepts like JOINs. Working knowledge of SQL (or Structured Query Language) is necessary for data professionals like Data Scientists, Data Analysts, and Data Engineers. Much of the world’s data resides in databases. SQL is a powerful language for communicating with and extracting data from databases. The estimated course completion time is 31 hours.
Module 1: Getting Started with SQL
Module 2: Introduction to Relational Databases and Tables
Module 3: Intermediate SQL
Module 4: Accessing Databases using Python
Module 5: Course Assignment
Module 6: Bonus Module: Advanced SQL for Data Engineering (Honors)
Course 7: Data Analysis with Python
Analyzing data with Python is essential for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models. You will learn to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful visualizations. You will then predict future trends from data by developing linear, multiple, and polynomial regression models & pipelines and learn how to evaluate them. The estimated course completion time is 14 hours.
Module 1: Importing Datasets
Module 2: Data Wrangling
Module 3: Exploratory Data Analysis
Module 4: Model Development
Module 5: Model Evaluation
Module 6: Final Assignment
Course 8: Data Visualization with Python
Learn vital data visualization skills in this course. Use tools like Matplotlib, Seaborn, and Plotly to discover ways to present data meaningfully. Create diverse charts (Bar, Pie, Scatter, etc.), interactive dashboards, and maps. Suitable for beginners. Practice through labs and a project, enhancing skills to communicate insights effectively. The estimated course completion time is 19 hours.
Module 1: Introduction to Data Visualization Tools
Module 2: Basic and Specialized Visualization Tools
Module 3: Advanced Visualizations and Geospatial Data
Module 4: Creating Dashboards with Plotly and dash
Module 5: Final Project and Exam
Course 9: IBM Data Analyst Capstone Project
Apply learned skills for the IBM Data Analyst Professional Certificate in this final project. As an Associate Data Analyst, I address real-world challenges through data analysis tasks like collection, wrangling, visualization, and dashboard creation. Conclude with a presentation of your report. Showcase proficiency in Jupyter, SQL, RDBMS, BI tools, and Python libraries. Enhance your portfolio and impress potential employers. The estimated course completion time is 20 hours.
Module 1: Data Collection
Module 2: Data Wrangling
Module 3: Exploratory Data Analysis
Module 4: Data Visualization
Module 5: Building A dashboard
Module 6: Final Assignment: Present Your Findings
Major Learnings
- Master the most up-to-date practical skills and tools that data analysts use in their daily roles.
- Learn how to visualize data and present findings using various charts in Excel spreadsheets and BI tools like IBM Cognos Analytics & Tableau.
- Develop a working knowledge of Python language for analyzing data using Python libraries like Pandas and Numpy, and invoke APIs and Web Services.
- Gain technical experience through hands-on labs and projects and build a portfolio to showcase your work.
Skills you will gain include Data Science, Spreadsheets, Microsoft Excel, Python Programming, Data Analysis, Pandas, Numpy, Data Visualization, Pivot Table, SQL, IBM Cognos Analytics, Dashboard, and more.
Applied Learning Projects:
Throughout the program, you’ll complete hands-on projects and labs and gain a firm grasp on the required technical skills to effectively gather, wrangle, mine, and visualize data, as well as the soft skills for working with stakeholders and storytelling with data to engage your audience.
Projects:
- Import, clean, and analyze fleet vehicle inventory with Excel pivot tables
- Use car sales key performance indicator (KPI) data to create an interactive dashboard with visualizations
- Extract and graph financial data with the Pandas data analysis Python library
- Use SQL to query census, crime, and school demographic data sets
- Wrangle data, graph plots, and create regression models to predict housing prices with data science Python libraries
- Create a dynamic Python dashboard to monitor, report, and improve US domestic flight reliability
Course Format and Duration:
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings, and assignments anytime and anywhere via the web or mobile. You will learn through videos, assessments, and hands-on labs. You’ll work with various data sources and project scenarios to gain practical data manipulation experience and apply analytical skills. This program includes over 145 hours of instruction and hundreds of practice-based assessments. Ideally, it will take 4 months to complete if you spend 10 hours a week. Moreover, this course has a flexible schedule, meaning everyone can learn at their own pace.
Certificates and Credentials:
Finally, you will earn a job-ready certificate after all your hard work. This Professional Certificate has an ACE® recommendation. This program is ACE® approved—when you complete it, you can earn up to 12 college credits, the equivalent of 5 college courses at the bachelor’s degree level. You can add these credentials to your LinkedIn profile, resume, or CV Share it on social media and in your performance review.
Pricing:
IBM Data Analyst Professional Certificate will cost you $ 39 per month. You can save money by increasing the course pace. If you complete the course in the recommended 4 months, it will cost you USD 156, and if you complete the course in 3 months, it will cost you $ 117 US. Enroll Now
Coursera also offers 7 days free trial with unlimited access to all courses in the Certificate. You can cancel the trial at any time with no penalties if you cancel before the trial ends if it’s not right for you.
Coursera Also offers financial aid in case you cannot bear the course fee. All you need to do is apply for financial aid and wait up to 16 days until your aid is approved.
Reviews from Other Users:
This professional certificate is rated 4.7 stars by 14K+ learners, which shows this course adds great value to the course graduate career. Based on the survey, 75% of certificate graduates report positive outcomes.
Community and Networking Opportunities:
In addition to expert-led training and hands-on projects designed to prepare you for a job, you’ll get access to an interview practice tool, mock interviews, resume-building workshops, career coaching sessions, and more.
Conclusion and Recommendation:
In conclusion, the IBM Data Analyst Professional Certificate is necessary for anyone seeking to thrive in today’s data-driven world. This comprehensive program imparts essential skills, empowering individuals to analyze, interpret effectively, and harness data for strategic decision-making. With a focus on hands-on experience, this beginner-friendly course covers Python, Excel, SQL, and more, ensuring job readiness in just four months. The program’s flexible schedule and affordable pricing make it accessible to all. Its ACE® recommendation, college credit potential, and strong user reviews reflect its value and impact. The networking and career-building opportunities further solidify this course as an invaluable investment in your future success.
Comparison with Similar Courses:
If you are interested in specifically learning want to become Google certified data analyst, I will recommend you take a look at the Google data analyst professional course review offered on Coursera.
