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Data Science with Python

The program builds a solid foundation in Data Science & Analytics by covering all real time industry standard tools and techniques through hands on, industry oriented curriculum. The program assumes no prior knowledge of coding in Python, or SQL and begins from fundamentals. By the end of the program the candidates have a deep understanding of statistical techniques critical to Data Analysis and they are able to create Analytical models using real life data to drive business impact.

Data Science with Python live online classes

20 Jul, 2020 Weekend Batch

Weekend Batch

Filling Fast

Timing - 11:00 to 14:00 PM (IST)
15 Jul, 2020 Mon to Fri Batch

Mon to Fri Batch

Filling Fast

Timing - 09:00 to 24:30 PM (IST)
23 Jul, 2020 Tus Batch

Tus Batch

Filling Fast

Timing - 24:30 PM to 24:00 PM (IST)
  • Statistics for Data Science 

    Module 1 :

    • Introduction to Statistics Descriptive and Inferential Statistics. Definitions , terms, types of data

    Module 2 :

    • Harnessing Data Types of Sampling Data. Simple random sampling, Stratified, Cluster sampling. Sampling error.

    Module 3 :

    • Exploratory Analysis Mean, Median and Mode, Data variability, Standard deviation, Z-score, Outliers

    Module 4 :

    • Distributions Normal Distribution, Central Limit Theorem, Histogram, Normalization, Normality tests, skewness, Kurtosis.

    Module 5 :

    • Hypothesis & computational Techniques Hypothesis Testing, Null Hypothesis, P-value, Type I & II errors, parametric testing: t- tests, anova test, non-parametric testing

    Module 6 :

    • Correlation & Regression
  • Machine Learning - Basics  

    Module 1 :

    • Machine Learning Introduction What is ML? ML vs AI. ML workflow, statistical modeling of ML. Application of ML

    Module 2 :

    • Machine Learning Algorithms Popular ML algorithms, clustering, classification and regression, supervised vs unsupervised. Choice of ML

    Module 3 :

    • Supervised Learning Simple and Multiple Linear regression, KNN, and more.

    Module 4 :

    • Linear Regression and Logistic Regression Theory of Linear regression, hands on with use cases

    Module 5 :

    • K-Nearest Neighbour (KNN)

    Module 6 :

    • Decision Tree

    Module 7 :

    • Naïve Bayes Classifier

    Module 8 :

    • Unsupervised Learning K-means Clustering.
  • Machine Learning Expert 

    Module 1 :

    • Advanced Machine Learning Concepts Tuning with Hyper parameters. Popular ML algorithms, clustering, classification and regression, supervised vs unsupervised. Choice of ML

    Module 2 :

    • Random Forest – Ensemble Ensemble theory, random forest tuning

    Module 3 :

    • Support Vector Machine (SVM) Simple and Multiple Linear regression, KNN,

    Module 4 :

    • Natural Language Processing (NLP) Text Processing with Vectorization, Sentiment analysis with TextBlob, Twitter sentiment analysis.

    Module 5 :

    • Naïve Bayes Classifier Naïve Bayes for text classification, new articles tagging

    Module 6 :

    • Artificial Neural Network (ANN) Basic ANN network for regression and classification

    Module 7 :

    • Tensorflow overview and Deep Learning Intro Tensorflow work flow demo and intro to deep learning.
  • Python for Data Science 

    Module 1 :

    • Introduction to Data Science with Python

    Module 2 :

    • Python Basics: Basic Syntax, Data Structures Installing Python, Programming basics, Native Data types Data objects, Math, comparison operators, condition statements, loops, lists, tuples, sets, dicts, functions

    Module 3 :

    • Numpy Package Overview, Array, selecting data, Slicing, Iterating, Manuplications, stacking, splitting arrays, functions

    Module 4 :

    • Pandas Package Overview, Series and DataFrame, manuplication.

    Module 5 :

    • Python Advanced: Data Mugging with Pandas Histogramming, grouping, aggregation, treating missing values, removing duplicates, Transforming data

    Module 6 :

    • Python Advanced: Visualization with MatPlotLib

    Module 7 :

    • Exploratory Data Analysis: Data Cleaning, Data Wrangling

    Module 8 :

    • Exploratory Data Analysis: Case Study
  • Time Series Analysis 

    Module 1 :

    • What is Time Series?,Trend, Seasonality, cyclical and random,White Noise,Auto Regressive Model (AR),Moving Average Model (MA), ARMA Model,Stationarity of Time Series

    Module 2 :

    • ARIMA Model – Prediction Concepts,ARIMA Model Hands on with Python,Case Study Assignment on ARIMA
  • Deep Learning - CNN Foundation 

    Module 1 :

    • REST API API concepts, web servers, URL parameters

    Module 2 :

    • FLASK Web framework Installing flask, configuration. Course

    Module 3 :

    • API in Flask 5+ Industry Projects API coding in Flask

    Module 4 :

    • End to End Deployment Exporting trained model, creating end to end API

Like the curriculum? Enroll Now

Structure your learning and get a certificate to prove it.

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Projects

  • How will I execute practical’s in GoSkills's Python Certification Course? 

    You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud Lab environment whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

  • What is CloudLab?  

    You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud Lab environment whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

    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.

  • Which case studies will be a part of this Python Certification Course ?  

    This 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 this course:

    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 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.

    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 user rents more books.

    You as an ML expert and must model recommendation engine so that 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

    Case Study 3: Handle missing values and fit a decision tree and compare its accuracy with 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.

    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.

    Case Study 5: Read the datafile “letterCG.data” and set all the numerical attributes as features. Split the data in to 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 decision tree classifier as the base classifier.

  • Which kind of projects will be a part of this Python Certification Course?  

    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 in Python 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.

    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 which explain the max variance.

Features

  • Feature

    Instructor-led Sessions

    Duration: 2 Months
    Week Day classes (M-F): 40 Sessions
    Daily 2 Hours per Session
  • Feature

    Real-life Case Studies

    Live project based on any of the selected use cases, involving the implementation of Data Science.
  • Feature

    Assignments

    Every class will be followed by practical assignments which aggregate to a minimum of 60 hours.
  • Feature

    Lifetime Access

    Lifetime access to Learning Management System (LMS) which has class presentations, quizzes, installation guide & class recordings.
  • Feature

    24 x 7 Expert Support

    Lifetime access to our 24x7 online support team who will resolve all your technical queries, through ticket based tracking system.
  • Feature

    Certification

    Successful completion of the final project will get you certified as a Data Science Professional by GoSkills.

FAQS

  • What if I miss a class?  

    You will never miss a lecture at GoSkill! You can choose either of the two options:

    • View the recorded session of the class available in your LMS.
    • You can attend the missed session, in any other live batch.
  • Will I get placement assistance?  
    • To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
  • Can I attend a demo session before enrollment?  
    • We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, quality of instructors and the level of interaction in a class.
  • Who are the instructors?  
    • All the instructors at GoSkill! are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.
  • What if I have more queries?  

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