![]() ![]() Logistic Regression and Linear Regression.Use NumPy for Numerical Data, Pandas for Data Analysis, Matplotlib for Python Plotting, Seaborn for statistical plots, Plotly for interactive dynamic visualisations, and scikit-learn for machine learning tasks.Python for Data Science and Machine Learning.Python for Data Science and Machine Learning Bootcamp Use Apache Spark to analyse data that does not fit within the memory of a single computer.Use machine learning to learn models for data.Make reliable statistical inferences from noisy data.Analyse the connectivity of a social network.Enhance a data analysis with applied machine learning.Discern whether a data visualisation is good or bad.Conduct an inferential statistical analysis.Data Modelling: Supervised/Unsupervised Learning and Model Evaluation, Feature Selection, Engineering, and Data Pipelines, Advanced Supervised/Unsupervised Learning, Advanced Model Evaluation and Data PipelinesĪpplied Data Science with Python Specialization by the University of Michigan.Exploratory Data Analysis and Visualisation.Perform regression analysis, least squares and inference using regression models.Use GitHub to manage data science projects.Navigate the entire data science pipeline from data acquisition to publication.Use R to clean, analyse, and visualise data.Listed below are some of the most trendy courses in 2022:ĭata Science Specialization by Johns Hopkins University It will help you get your fundamentals right while enabling you to learn the required skills at your own pace. Ways to build a data science portfolioįrom a theoretical perspective, massive open online courses (MOOCs) are perhaps a good place to start. A diverse, well-groomed portfolio can make all the difference in getting a job!” said Mohan C R, Project Engineer, Wipro. When you put these up in your resume or on platforms like GitHub, Deepnote, and Kaggle or your portfolio website, recruiters can see your capabilities first hand. They should cover concepts like supervised learning, unsupervised learning, deep learning, etc. “A portfolio must essentially have projects that show your interest or expertise in different areas of data science. It is essentially public evidence of the projects you’ve worked on, showcasing your creative, technical, and soft skills, approach to effectively analysing data and drawing insights, and ability to communicate the outcome to audiences. Think of a data science portfolio as an extension to your resume. What you need is a data science portfolio. And if you want to stand out, you need to do more than the bare minimum. But, that’s not enough!Īccording to Michael Page India’s ‘The Humans of Data Science’ report, data science is poised to create more than 11.5 million job openings by 2026. And if you are lucky, then an internship might be included. Most of these institutes collaborate with SMEs to prepare a curriculum that exposes students to subjects like data analytics, machine learning, business analysis, statistics, data modelling, data visualisation, cloud computing, database systems, and many more alongside programming languages such as Python, JavaScript, Scala, R, SQL, and Julia. More than 596 data science, big data & analytics courses are offered by nearly 470 colleges pan-India. To get the sexiest job of the 21st century, it’s pivotal to first understand the prerequisites – strong analytical and computational skills and start finding ways to hone in on them.įrom an academic standpoint, data science is flourishing. If you are entering higher ed and envision becoming a data scientist, you’re probably wondering where to start. ![]()
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