Data Science with Python Certification Course

Skills:

Skills:

Skills:

Skills:

Python has been one of the premier, flexible, and powerful open-source languages that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, It has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger. It has evolved as the most preferred Language for Data Analytics and the increasing search trends also indicate that it is the Next Big Thing and a must for Professionals in the Data Analytics domain.

After completing this Data Science using Python Certification course, you will be able to:

Edureka offers the best online course for Python Data Science. Enroll now with our data Science with Python training and get a chance to learn from industrial giants.

Edureka’s course is a good fit for the below professionals:

The pre-requisites for Edureka's Python Data Science course training includes the fundamental understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be a plus. However, you will be provided with complimentary “Python Statistics for Data Science” as a self-paced course once you enroll for the Data Science with Python certification course.

CloudLab is a cloud-based Jupyter Notebook which is pre-installed with Python packages on the cloud-lab environment. It is offered by Edureka as a part of Python Data Science Course where you can execute all the in-class demos and work on real-life projects in a fluent manner. You’ll be able to access the CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support ninja team is ready to assist 24x7.

Edureka's Data Science with Python certification course comprises of 40 case studies that will enrich your learning experience. In addition, we also have 4 Projects that will enhance your implementation skills. Below are few case studies, which are part of the Data Science with Python course curriculum: Data Science with Python Certification Case Study 1: Maple Leaves Ltd is a start-up company which makes herbs from different types of plants and its leaves. Currently, the system they use to classify the trees which they import in a batch is quite manual. A laborer from his experience decides the leaf type and subtype of the plant family. They have asked us to automate this process and remove any manual intervention from this process. You have to classify the plant leaves by various classifiers from different metrics of the leaves and to choose the best classifier for future reference. Data Science with Python Certification Case Study 2: BookRent is the largest online and offline book rental chain in India. The company charges a fixed fee per month plus rental per book. So, the company makes more money when the user rents more books. You as an ML expert must model a recommendation engine so that the user gets a recommendation of books based on the behavior of similar users. This will ensure that users are renting books based on their individual taste. The company is still unprofitable and is looking to improve both revenue and profit. Compare the Error using two approaches – User Based Vs Item Based Data Science with Python Case Study 3: Handle missing values and fit a decision tree and compare its accuracy with a random forest classifier. Predict the survival of a horse based on various observed medical conditions. Load the data from ‘horses.csv’ and observe whether it contains missing values. Replace the missing values by the most frequent value in each column. Fit a decision tree classifier and observe the accuracy. Fit a random forest classifier and observe the accuracy. Data Science with Python Case Study 4: Principal component analysis using scikit learn. Load the digits dataset from sklearn and write a helper function to plot the image. Fit a logistic regression model and observe the accuracy. Using scikit learn perform a PCA transformation such that the transformed dataset can explain 95% of the variance in the original dataset. Compare it with a model and also comment on the accuracy. Compute the confusion matrix and count the number of instances that have gone wrong. For each of the wrong sample, plot the digit along with the predicted and original label. Data Science with Python Case Study 5: Read the datafile “letterCG.data” and set all the numerical attributes as features. Split the data into train and test sets. Fit a sequence of AdaBoostClassifier with varying number of weak learners ranging from 1 to 16, keeping the max_depth as 1. Plot the accuracy on the test set against the number of weak learners, using the decision tree classifier as the base classifier.

Data Science with Python Project #1: Industry: Social Media Problem Statement: You as ML expert have to do analysis and modeling to predict the number of shares of an article given the input parameters. Actions to be performed: Load the corresponding dataset. Perform data wrangling, visualization of the data and detect the outliers, if any. Use the plotly library to draw useful insights out of data. Perform regression modeling on the dataset as well as decision tree regressor to achieve your Learning Objectives. Also, use scaling processes, PCA along with boosting techniques to optimize your model to the fullest. Data Science with Python Project #2: Industry: FMCG Problem Statement: You as an ML expert have to cluster the countries based on various sales data provided to you across years. Actions to be performed: You have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that, you have to bring the exports (in tons) of all countries down to the same scale across years. Plus, as this solution needs to be repeatable you will have to do PCA so as to get the principal components that explain the max variance.