Getting a job as a data analyst is just the first step in a rewarding career in data. Let's take a closer look at four common career paths you can take once you get started in this high-demand field.
Getting a job as a data analyst is just the first step in a rewarding career in data. In this video, let's take a closer look at four common career paths you can take once you get started in this high-demand field.
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Learn in-demand skills like statistical analysis, Python, regression models, and machine learning in less than 6 months.
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Tableau Software, Descriptive Statistics, Sampling (Statistics), Object Oriented Programming (OOP), Data Storytelling, Data Visualization, Feature Engineering, Machine Learning, Advanced Analytics, Data Presentation, Python Programming, Data Science, Statistical Hypothesis Testing, Data Ethics, Data Analysis, Data Visualization Software, Interviewing Skills, Exploratory Data Analysis, Regression Analysis, Statistical Analysis, Scripting, Data Structures, Programming Principles, NumPy, Pandas (Python Package), Data Manipulation, Algorithms, Artificial Intelligence, Business Analysis, Machine Learning Methods, Predictive Modeling, Project Portfolio Management, Variance Analysis, Statistical Modeling, Probability & Statistics, Analytical Skills, Business Analytics, Scikit Learn (Machine Learning Library), Supervised Learning, Correlation Analysis, Random Forest Algorithm, Unsupervised Learning, Machine Learning Algorithms, Performance Metric, Decision Tree Learning, Performance Tuning, Classification And Regression Tree (CART), Workflow Management, Applicant Tracking Systems, Prompt Engineering, Communication, Personal Attributes, Professional Development, Problem Solving, Generative AI, Communication Strategies, Analytics, Data-Driven Decision-Making, Data Literacy, Business Ethics, Project Management, Stakeholder Communications, Data Cleansing, Data Validation, Data Quality, Data Processing, Data Transformation, Technical Communication, Statistical Inference, Statistics, Probability, A/B Testing, Probability Distribution, Statistical Methods, Statistical Programming
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Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.
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Jupyter, Generative AI, Data Wrangling, Matplotlib, Unsupervised Learning, Plotly, Data Visualization, Predictive Modeling, SQL, Feature Engineering, Data Analysis, Data Visualization Software, Supervised Learning, Data Mining, Exploratory Data Analysis, Dashboard, Professional Networking, Pandas (Python Package), Interactive Data Visualization, Data Literacy, Regression Analysis, Descriptive Statistics, Data Manipulation, Scikit Learn (Machine Learning Library), Statistical Modeling, Data Pipelines, NumPy, Data Cleansing, Data Import/Export, Python Programming, Data-Driven Decision-Making, Data Synthesis, Predictive Analytics, Data Presentation, Data Science, Natural Language Processing, Data Ethics, Data Storytelling, Data Modeling, R Programming, GitHub, Git (Version Control System), Machine Learning, Big Data, Other Programming Languages, Query Languages, Statistical Programming, Cloud Computing, Application Programming Interface (API), Version Control, Classification And Regression Tree (CART), Dimensionality Reduction, Decision Tree Learning, Applied Machine Learning, Relational Databases, Databases, Stored Procedure, Database Management, Database Design, Transaction Processing, Interviewing Skills, Applicant Tracking Systems, Portfolio Management, Professional Development, Recruitment, Presentations, Communication, Talent Sourcing, Business Research, Writing, Job Analysis, Problem Solving, Company, Product, and Service Knowledge, Deep Learning, Digital Transformation, Artificial Intelligence, Histogram, Scatter Plots, Seaborn, Box Plots, Geospatial Information and Technology, Heat Maps, Data Structures, Object Oriented Programming (OOP), File Management, Web Scraping, Programming Principles, Restful API, Computer Programming, Data Processing, Data Collection, Data Quality, Business Analysis, Analytical Skills, User Feedback, Stakeholder Engagement, Peer Review, Machine Learning Methods
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