Curious about data science? With data scientists ranking among the top jobs in the US that's growing, there's no better time to explore this rewarding career path.
With data scientists ranking among the top jobs in the US with high growth, there's no better time to explore this rewarding career path. In today’s video, we’re breaking down three essential steps to get you started on your journey to becoming a data scientist—whether you have a degree or are looking for alternative pathways.
📚Ready to get started? Start building job-ready skills with one of these programs from industry leaders in data science:
professional certificate
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.
4.6
(79,120 ratings)
722,015 already enrolled
Beginner level
Average time: 4 month(s)
Learn at your own pace
Skills you'll build:
Data Science, Generative AI, Predictive Modelling, Data Analysis, Model Selection, Data Visualization, Python Programming, Pandas, Numpy, Dashboards and Charts, Matplotlib, dash, Relational Database Management System (RDBMS), Cloud Databases, Jupyter notebooks, SQL, regression, Clustering, SciPy and scikit-learn, classification, Machine Learning, CRISP-DM, Data Mining, Methodology, K-Means Clustering, Github, Jupyter Notebook, Data Science Methodology, Rstudio, Deep Learning, Big Data, Quering Databases, Data Generation, Interviewing Skills, Resume Building, Career Development, Job Preparation
specialization
#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng
4.9
(32,764 ratings)
612,515 already enrolled
Beginner level
Average time: 2 month(s)
Learn at your own pace
Skills you'll build:
Algorithms, Artificial Neural Network, Mathematics, Human Learning, Linear Regression, Machine Learning, Artificial Neural Networks, Deep Learning, Critical Thinking, Recommender Systems, Network Model, Regression, Decision Trees, Applied Machine Learning, Machine Learning Algorithms, Logistic Regression, Python Programming, Advice for Model Development, Tensorflow, Tree Ensembles, Xgboost, Supervised Learning, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, Collaborative Filtering, Anomaly Detection, Reinforcement Learning, Unsupervised Learning
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