Learn To Develop & Deploy Machine Learning, Data Science, Deep Learning Projects
Data Science is centered on building, cleaning, and organizing datasets. Data scientists create and leverage algorithms, statistical models, and their own custom analyses to collect and shape raw data into something that can be more easily understood.
Data Scientists lay the groundwork for all of the analyses an organization performs. They do this by performing key functions, including:
- Data wrangling: The process of cleaning and organizing data to be more readily used.
- Statistical modeling: The process of running data through different models—such as regression, classification, and clustering models, among others—to identify relationships between variables and gain insight from the numbers.
- Programming: The process of writing computer programs and algorithms in a variety of languages—such as R, Python, and SQL—that can be used to analyze large datasets more efficiently than through manual analysis.
Learn about Big Data, Data Analytics, Data Mining, Data Algorithm
Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.
- Predictive causal analytics – If you want a model that can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model that can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
- Prescriptive analytics: If you want a model that has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
- Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
- Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
- Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.
Project-1 Sentiment Analysis App (With Deployment)
Project-2 Attrition Rate Django App
Project-3 App To Find Legendary Pokemon (With Deployment)
Project-4 Face Detection App (Deploy In Streamlit)
Project-5 Cats Vs Dogs Classification App
Project-6 Customer Revenue Prediction App (With Deployment)
Project-7 Gender From Voice Prediction App
Project-8 Restaurant Recommendation System
Project-9 Happiness Ranking App
Project-10 Forest Fire Prediction App
Who this course is for:
- Anyone interested in understand and learning about Data Science
- Anyone plan to enter and strategies to embrace Data Science
- Creators and Developers who want to start prepare for Data Science
- Anyone wish to develop real world Data Science project
Additionally, a proficiency in key data science skills can enable you to assess and draw insights from your organization’s data—increasing the value you bring to your organization while reducing your reliance on others. Developing your data science skills can allow you to:
- Identify and avoid mistakes that commonly arise while interpreting datasets, metrics, and visualizations
- Embrace data-driven decision-making and ensure your business decisions are backed by numbers
- Form hypotheses, run experiments, and gather evidence that empowers you to recognize business challenges and solutions
- Understand market size, buyer trends, competition, and opportunities and risks your business faces