The data science projects that you mention in your portfolio should be a clear reflection of your strong quantitative reasoning and problem solving skills using concepts of math and statistics. To flesh out your data science portfolio with projects you will have to work on small data science projects hosted on Kaggle or enrol for ProjectPro Hackerday where you can work on 4 projects every month under expert guidance.  Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again. View Project Details. Time Series Forecasting with LSTM Neural Network Python. Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data. View Project Details. A comprehensive guide to creating a stellar Data Science portfolio. Harshit Tyagi. Follow.  Building End-to-End projects: A great way of proving that you are truly a generalist is to build end-to-end projects(more like products). Don’t stop at finding the solution or creating a prototype for a recommendations system or a fintech chatbot, go the extra mile, deploy it, share it with your peers to use it, collect some analytics.  I have been working on creating projects for each profile based on my experience working as an Instructional Designer for Web and Data Science tracks. Based on your response to this post, I will create a Discord channel for each profile where I’ll be sharing the projects and the instructions to complete them with the timeline associated with each. GitHub is where people build software. More than 56 million people use GitHub to discover, fork, and contribute to over million projects.  Portfolio of data science projects completed by me for academic, self learning, and hobby purposes. python portfolio data-science data machine-learning data-visualization datascience data-analysis data-science-portfolio. Updated Aug 21,   This Portfolio is a compilation of all the Data Science and Data Analysis projects I have done for academic, self-learning and hobby purposes. This portfolio is updated on the regular basis. machine-learning skills certificates projects data-science-portfolio. Find out more here. Whether it's automated machine learning, interactive notebooks and report generation, natural language queries of data for instant visualizations or implementing neural networks with ease and efficiency, modern problem solving requires access to the right technology at every stage. This Professional Certificate from IBM will help anyone data science projects to build portfolio network in pursuing a career in data science or machine learning develop career-relevant skills and experience. Offered By. With a free account, there are limitations on the number of member articles that you can actually access per month.

I am sure you will use it a lot. Download this dataset from this link. An amazing dataset for learners. The column names of this dataset may not look very understandable at first. But once you get used to them, you can use this one dataset to practice Data Analysis, Visualization, Statistical Modeling, and Machine Learning models both classification and regression. Download it from here. It contains Wikipedia profiles of some famous people.

The dataset contains three columns: URI, name name of the person , and text it includes the Wikipedia profile. A simple but very useful dataset for Natural Language Processing. Please check out this article to see an example of what you can do with this dataset:. Here is the link to this dataset. This dataset contains millions of product reviews of the products of amazon. It has three columns: Name of the product, review, and rating.

This dataset is almost a real dataset, very good for N atural Language Processing. I have a sentiment analysis project and an article where I used this dataset. Please check it out here:. I used this dataset for this project:. Here is the link. This is one of the most common datasets to develop Regression Models.

For sure you can use it for other purposes as well. This is mostly used to predict the housing prices based on the information in the other columns.

I used it for Classification problems. It can be used for other purposes as well. It contains these columns: class, cap-shape, cap-surface, cap-color, bruises, odor, gill-attachment, gill-spacing, gill-size, gill-color, stalk-shape, stalk-root, stalk-surface-above-ring, stalk-surface-below-ring, stalk-color-above-ring, stalk-color-below-ring, veil-type, veil-color, ring-number, ring-type, spore-print-color, population, habitat.

This dataset has information on the Olympic results. Each row contains the data of a country. This dataset will give you a taste of data cleaning to start with.

Another very popular dataset. I myself used it a lot, I saw different experienced people using this dataset to present a concept. This is a tutorial where I used this dataset:.

Another widely used dataset in data science courses. This one is especially good for learning Classification Models. We used for Classification Models. A credit card fraud detection project looks good in a portfolio. As students progress in the program, they master more advanced concepts and have the opportunity to choose a specialization with a customized curriculum that closely aligns with their goals.

By choosing an area of specialization, students can master necessary skill sets and apply them directly to their career interests. Master the machine learning techniques needed to build self-optimizing systems and provide solutions to problems or improve processes in any organization. Learn more about the Machine Learning Specialization.

Master the analytical tools required to Data Science Projects To Build Portfolio China synthesize qualitative data and effectively communicate results to key stakeholders to inform strategic decision-making. Learn more about the Business Analytics Specialization. Analyzes machine learning and the data preparation workflow, including multivariate nonlinear nonparametric regression, supervised classification, unsupervised classification and deep learning.

All material covered is reinforced through hands-on experiences using state-of-the-art tools to design and execute data mining processes using Python and R. Explores natural language processing NLP as applied to data mining, text mining and machine learning tasks with unstructured big data.

Topics include document clustering and classification, automated tagging and highlighting, semantic search and text normalization to support machine learning applications. Students will gain experience building solutions from real-world data sets, utilizing WordNet and the data of some leading websites. Examines the practical applications of the use of data sciences in the application of econometrics and quantitative finance.

The primary learning framework is based on utilization of real and simulated data sets for business and economic situations. Students become familiar with the use of R in the creation of data analysis combining financial theory and statistical analysis, including portfolio theory, CAPM and econometric modeling.

The field of data science is one of the fastest-growing and most in-demand fields in the world. DataScience SMU students gain highly sought-after skills in working with unstructured data, big data processing, statistical analysis, text mining and machine learning.

With the ability to synthesize findings into actionable results for their organizations, our graduates are able to enter the data science field as candidates with a competitive edge. Data scientists use data to tell compelling stories to inform business decisions. Learn more about what data science is and what data scientists do in the first course of this Professional Certificate, "What is Data Science? An understanding of data science and the ability to make data driven decisions is useful in any career, but some careers specifically require a data science background.

Some examples of careers in data science include:. The Professional Certificate requires completion of 9 courses. Each course typically contains modules with an average effort of 2 to 4 hours per module. If learning part-time e. If learning full-time e.

This Professional Certificate is open for anyone with any job and academic background. No prior computer programming experience is necessary, but is an asset, as are familiarity working with computers, high school math, and communication and presentation skills. For the last few courses, knowledge of calculus and linear algebra is an asset but not an absolute requirement.

Yes, it is highly recommended to take the courses in the order they are listed, as they progressively build on concepts taught in previous courses. For example the Data Analysis, Data Visualization, and Machine Learning courses require knowledge of Python covered earlier in the program. Become job ready for a career in Data Science. Develop practical skills using hands-on labs in Cloud environments, projects and captsones.

I already completed some of the other courses in this Professional Certificate. Will I get "credit" for them? If you have already completed some of the courses in this Professional Certificate, either individually or as part of another Specialization, they will be marked as "Complete".

You do not have to take those courses again, and will be able to finish the Professional Certificate more quickly. You will only need to complete the courses that you have not yet completed.

I have already completed the Introduction to Data Science Specialization. Can I still enroll in this Professional Certificate?

Yes, absolutely. Any courses that you have already completed as part of that Specialization will be marked as "Complete". You do not have to take those courses again and will be able to finish the Professional Certificate more quickly.

This Professional Certificate consists of 9 courses. The "Introduction to Data Science" Specialization has 4 courses, all of which are also included in this Professional Certificate. If you are unsure about your ability to commit to the level of effort and time required to complete this Professional Certificate, we recommend starting with the Introduction to Data Science Specialization, which has fewer courses.

If, after completing the Specialization, you are still determined to continue building your data science skills, you can then enroll for this Professional Certificate and then just complete the courses that are not in the Specialization. I have already completed the Applied Data Science Specialization. You do not have to take those courses again and will be able to finish this Professional Certificate more quickly.

Our Talent Network members receive all of the tools you need to land a dream job with IBM - sent directly to your inbox! You will get job opportunities as soon as they are posted, recommendations to apply matched directly to your skills and interests, and tips and tricks to help you stand apart from the crowd.

More questions? Visit the Learner Help Center. Data Science. Data Analysis. Offered By.

Kreg Jig 45 Degree Joints Instagram Do It Yourself Vertical Wall Garden Test