Data Science with Python Training in Hyderabad

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Python is among the most popular, flexible, and powerful open-source languages that are easy to master, easy to learn, and has a highly robust library for data analysis and manipulation. For over a decade, the language has been utilized in scientific computing and other highly quantitative fields like finance, oil and gas and physics, and signal processing. It's been a preferred choice for data scientists who utilize it to develop and run machine-learning programs and other scientific computations. Python can cut development time by half due to its simple syntax and compilation. Debugging programs is easy using Python due to the built-in debugger. It's now the most popular choice in Data Analytics. Recent searches indicate that it's becoming one of the Next Big things and an absolute necessity for professionals involved in the Data Analytics domain.

Upon completion of Data Science using Python Certification course, You can:

• Programmatically download and analyze data
• Learn to handle various kinds of information: categorical, ordinal, and encode
• Learn data visualization
• With Python notebooks, learn how to present the step-by-step analysis of data
• Gain a better understanding of the roles played by Machine Learning Engineers. Machine Learning Engineer
• Define Machine Learning
• Use real-time data
• Learn about the tools and techniques to model predictively.
• Discussion about how to apply Machine Learning algorithms and their applications.
• Verify the Machine Learning algorithms
• Perform the following duties: Text Mining as well as Sentimental Analysis
• Let us discuss the Time Series and its related concepts.
• Learn to run your business's future while still living the present.

Edureka is the most effective online Course to master Python for Data Science. Join us today to Data Science with Python training and the chance to learn from industrial giants.

Edureka's Course is an excellent course for people who are not at the professional level:

• Developers and Programmers, Technical Leads Architecture, Developers
• Developers who wish to become an engineer in machine learning.
• Analytics Managers, who oversee the analysts team
• Business Analysts who want to grasp Machine
• Learning (ML) Techniques
• Information Architects who want to learn more about
• Predictive Analytics
• Professionals who would like to create automated models for predictive purposes

Edureka's Data Science course in Hyderabad prerequisites include a basic understanding of Computer Programming Languages. The basics for Data Analysis practiced over any instruments for data analysis such as SAS/R can be a benefit. You will also receive the free "Python statistical techniques for data science" as a self-paced Course when you enroll in The Data Science with Python certification course in Hyderabad.

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.