Is Data Analytics Really That Hard? Tips for Rising to the Challenge

Written by Coursera Staff • Updated on

This video is here to tell you - learning data analytics IS achievable, and we'll show you how!


[Video thumbnail] Tips to master data analytics

Thinking about a data analytics career but feeling intimidated by the technical skills? You're not alone! This video is here to tell you - learning data analytics IS achievable, and we'll show you how!

We'll bust myths about data analytics being "too hard" and share practical tips to conquer the learning curve:

  • Reframing "Difficult" Skills: See them as investments in a lucrative, high-demand career.

  • Step-by-Step Learning: Avoid overwhelm by focusing on one skill or concept at a time.

  • Hands-On Practice: Build real projects and don't be afraid to make mistakes - that's how we learn!

  • The Power of Community: Find support and inspiration from fellow learners online.

  • Embrace Continuous Learning: Data is always evolving - stay curious and keep those skills sharp.

Ready to unlock your inner data hero? We've included some amazing courses below to get you started! 👇

Google

professional certificate

Google Data Analytics

Get on the fast track to a career in Data Analytics. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.

4.8

(157,842 ratings)

2,857,067 already enrolled

Beginner level

Average time: 6 month(s)

Learn at your own pace

Skills you'll build:

Tableau Software, Interviewing Skills, Sampling (Statistics), Presentations, Rmarkdown, Data Cleansing, Data Ethics, Data Storytelling, Stakeholder Communications, Interactive Data Visualization, Applicant Tracking Systems, LinkedIn, Data Validation, Data Visualization, Ggplot2, Data Presentation, Data Literacy, Data Analysis, Spreadsheet Software, Data Visualization Software, Data-Driven Decision-Making, SQL, Analytical Skills, Data Sharing, Google Sheets, Data Management, Data Processing, Data Integrity, Data Quality, Data Transformation, Sample Size Determination, Generative AI, Problem Solving, Professional Development, Prompt Engineering, Personal Attributes, Communication, Dashboard, Quantitative Research, Expectation Management, Business Analysis, R Programming, Data Manipulation, Integrated Development Environments, Programming Principles, Tidyverse (R Package), Data Structures, Statistical Programming, Pivot Tables And Charts, Excel Formulas, User Feedback, Data Compilation, Analytics, Data Integration, Research Reports, Business Analytics, Portfolio Management, Artificial Intelligence, Data Security, Data Collection, Databases, Relational Databases, Unstructured Data, Data Storage, Web Content Accessibility Guidelines

Meta

professional certificate

Meta Data Analyst

Launch your career in data analytics. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from Meta in 5 months or less. No degree or prior experience required.

4.7

(774 ratings)

38,387 already enrolled

Beginner level

Average time: 5 month(s)

Learn at your own pace

Skills you'll build:

Key Performance Indicators (KPIs), Data Management, Descriptive Statistics, SQL, Data Cleansing, Data Governance, Exploratory Data Analysis, Data Storytelling, Statistical Hypothesis Testing, Bayesian Statistics, Python Programming, Data Collection, Data Visualization, Data Presentation, Pandas (Python Package), Business Metrics, Data Analysis, Spreadsheet Software, Data Visualization Software, Information Privacy, Data Manipulation, Programming Principles, Matplotlib, Scripting, Jupyter, Data Modeling, Data Processing, Statistics, Sampling (Statistics), Time Series Analysis and Forecasting, Statistical Modeling, Quantitative Research, Tableau Software, Probability & Statistics, Statistical Analysis, Statistical Methods, Data Analysis Software, Descriptive Analytics, Statistical Inference, Analytics, Marketing Analytics, Data Storage, Data Security, Data Quality, Machine Learning, Data-Driven Decision-Making, Big Data, Data Architecture, Data Validation, Generative AI, Business Analysis, Marketing, Google Sheets, Correlation Analysis, Dashboard, Pivot Tables And Charts

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