Data Science Using Python
PRICE: 39,500
Sessions : 26       Hours: 208
Inclusive all of taxes
Data Science starts from EDA (Exploratory Data Analysis) and covers all the techniques for analytics keeping in mind weather it will be logistic or Linear. It will cover complete work flow of eCommerce.

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Timings - 03:00 PM to 07:00 PM (IST)



Timings - 03:00 PM to 07:00 PM (IST)



Timings - 09:00 AM to 01:00 PM (IST)



Timings - 02:00 PM to 06:00 PM (IST)

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Data Science Using Python Curriculum

  • What is Data Analytics & Data Science
  • Different types of Data Analytics (Descriptive, Predictive, Prescriptive)
  • What is Artificial Intelligence
  • What is IOT
  • Machine Learning (Supervised & Unsupervised Learning)
  • Deep Learning (Artificial Neural Networks, CNN)
  • Working with multiple data sources – RDBMS (SQL Server, Oracle, My SQL, DB2), 
    NOSQL (MongoDB, Cassandra, CouchDB)
  • Real world Applications of Machine Learning & Deep Learning
  • What to expect from this course (Salary, Market trends, job roles, Domain)

  • Overview of ecommerce industry
  • Concepts of Warehousing, Distance traversing
  • Route Optimization, Load Balancing, Distribution Channels
  • Concept of Unit Economics
  • Time Series prediction of load
  • How data science plays a vital role in ecommerce & logistics

  • Overview of ecommerce Data
  • Data Structure & Understanding
  • Scope of exploratory Analysis
  • Understanding Biases & Inherent Error
  • Mandate for Predictive Modelling (Exploratory Analysis)
  • Goal Statement: Predicting if the customer will accept the shipment or return it 

Introduction to Python

  • Overview
  • The Python Ecosystem
  • Why Python over R/SAS
  • What to expect after you learn Python

Getting Started

  • Understanding and choosing between different Python versions
  • Setting up Python on any machine (Windows/Linux/Mac)
  • Using Anaconda, the Python distribution
  • Exploring the different third-party IDEs (PyCharm, Spyder, Jupyter, Sublime)
  • Setting up a suitable Workspace
  • Running the first Python program

  • Python Syntax
  • Interactive Mode/ Script Mode Programming
  • Identifiers and Keywords
  • Single and Multi-line Comments
  • Data Types in Python (Numbers, String, List, Tuple, Set, Dictionary)
  • Implicit and Explicit Conversions
  • Understanding Operators in Python
  • Working with various Date and Time formats
  • Working with Numeric data types – int, long, float, complex
  • String Handling, Escape Characters, String Operations
  • Working with Unicode Strings
  • Local and Global Variables

  • Flow Control and Decision Making in Python
  • Understanding if else conditional statements
  • Nested Conditions
  • Working in Iterations
  • Understanding the for and while Loop
  • Nested Loops
  • Loop Control Statements– break, continue, pass
  • Understanding Dictionary- The key value pairs
  • List Comprehensions and Dictionary Comprehensions
  • Functions, Arguments, Return Statements
  • Packages, Libraries and Modules
  • Error Handling in Python

Working with Data - Data from External Sources

  • Reading data from files (TXT, CSV, Excel, JSON, KML etc.)
  • Writing data to desired file format
  • Creating Connections to Databases
  • Importing/Exporting data from/to NoSQL databases (MongoDB)
  • Importing/Exporting data from/to RDBMS (PostgreSQL)
  • Getting data from Websites
  • Manipulating Configuration files

Data Wrangling

  • Introduction to Data Wrangling Techniques
  • Why is transformation so important
  • Understanding Database architecture – (RDBMS, NoSQL Databases)
  • Understanding the strength/limitations of each complex data containers
  • Understanding Sorting, Filtering, Redundancy, Cardinality, Sampling, Aggregations
  • Converting from one Data Type to another

  • Introduction to Numpy and its superior capabilities
  • Understanding differences between Lists and Arrays
  • Understanding Vectors and Matrices, Dot Products and Matrix Products
  • Universal Array Functions
  • Understanding Pandas and its architecture
  • Getting to know Series and DataFrames, Columns and Indexes
  • Getting Summary Statistics of the Data
  • Data Alignment, Ranking & Sorting
  • Combining/Splitting DataFrames, Reshaping, Grouping
  • Identifying Outliers and performing Binning tasks
  • Cross Tabulation, Permutations, the apply() function
  • Introduction to Data Visualization
  • Line Chart, Scatterplots, Box Plots, Violin Plots
  • Understanding Probability Distribution
  • Histograms, Heat maps and Clustered Matrices
  • Plotting Kernel Density Estimate Plots

  • Introduction to VCS
  • Why is it Absolutely Necessary for a programmer to use VCS
  • Understanding the concepts of GIT
  • Collaborative development using GIT
  • Understanding services like GitHub and BitBucket
  • Thorough understanding of all the GIT commands (pull, clone, status, commit, push, merge etc.)
  • Creating and Maintaining repositories for Projects
  • How can you contribute to Open-Source

  • Continuous and Discrete Variables
  • Understanding Distributions
  • Standard Deviation, Normal Distribution, Skewness
  • Mean, Median, Mode
  • Comparing Infinities
  • Cantor’s Diagonal Argument
  • Understanding the concept of Population and Samples
  • Sampling Distribution
  • Central Limit Theorem – Intuition & Visualization
  • Calculating Z-Score
  • Introduction to Hypothesis Testing
  • Assumptions, Rejection Region
  • Calculating Statistical Significance
  • Understanding Null Hypothesis and T-Distribution
  • Introduction to Pareto Principle

Introduction to Machine Learning

  • What is Machine Learning
  • Applications of Machine Learning
  • Setting up the working environment

Data Pre-Processing

  • Importing the Dataset
  • Handling missing data
  • Handling Categorical data
  • Splitting the dataset into training and test dataset
  • Feature Scaling

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression

  • Logistic Regression
  • K-Nearest Neighbours (K-NN)
  • Support Vector Machines
  • Kernel SVM
  • Naive Bayes Classifier
  • Decision Tree Classification
  • Random Forest Classification

Unsupervised Learning

  • Clustering – Intuition
  • K-Means Clustering
  • Hierarchical Clustering

Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Understanding Kernel PCA

Model Selection & Boosting

  • Understanding the need for Model Selection
  • What is Overfitting
  • Understanding Bias Variance Trade-off
  • K-Fold Cross Validation
  • Understanding and applying Grid Search

XGBoost in Python (Bonus Lecture)

Deep Learning

  • Introduction to Deep Learning
  • The Human Brain and how it works

  • Project: Predicting if the customer will accept the shipment or return 

What next   Artificial Neural Networks (Using Theano, Tensorflow, Keras)

  • Neurons
  • Understanding the Activation Function
  • How neural networks learn
  • Understanding Stochastic Gradient Descent
  • Concept of Backpropagation

Convolutional Neural Networks

  • Introduction to Convolutional Networks
  • Understanding Convolutional Operations
  • Understanding Pooling, Flattening
  • Softmax & Cross-Entropy

  • Introduction to Data Visualization with Tableau
  • Data Import and Management
  • Data Type and Operation
  • Visualizations Deep Dive
  • Data Organization and Scripting
  • Playing with Time Dimension
  • What is Your Location?
  • Incremental Loading and Blending
  • The World is Your Visualization
  • Statistical Analysis with Tableau & R
  • Sharing Insights with Enterprise Dashboards
  • Analyse Project data and extract meaningful information from it
  • Create Dashboards and Stories from data sets

  • A list of Orders returned by the Customers (in terms of refund).
  • Top 10 Countries mapped on the World Map that had most of the refunds.
  • Predict the refund for next 1 year category-wise.
  • Actions to be Performed to Create the Dashboard
  • Create hierarchies and folders in the dataset provided.
  • Generate a list of order returned from customers and compare it to the original sales, sort the visualization in ascending. 
  • Order in terms of returned order, for top 10 countries in terms of refunds.
  • Map top 10 countries, with most of the refunds on the world map.
  • Predict the returned sale with lowest and actual forecast of the data
  • Add a URL action to represent details of the countries
  • Finally publish your work to Tableau Server/ Online

Data Science Using Python Reviews

Palin Analytics is Best online Data Science Training Institute in Gurgaon. complete advanced curriculum with real-time projects and assignments which helps us to learn the deep subject.

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In this training i learned alot related to data science. Tushar was my trainer, his knowledge is realy awsome and he is very dedicated towards his responsibility. Dedicated and synchronized..

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Great learning environment. Trainers are industry professionals and have indepth knowledge.Training was highly knowledgeable. It helped me to enhance my skills and achieve my career goals.

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Kumar Saurabh

My experience with Palin institute was quite good and faculty is also very cooperative. Good to gain knowledge for analytical skills

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Monica Pal

They ameliorated my knowledge which helped me through. Environment is serene.One can go for it without doubts.

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Anjali Dalal

Good Institute to learn Datascience and Statistical Techniques various tools . Experienced faculty , good evironment and nice infrastructure

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Rohit Tak

Data Science Using Python Project



Min 8+ years of industry Experience.For end to end implementation in analytics life cycle


Training as per industry requirement with mock interviews training


Designed by professionals according to industry requirement


Choose the timimg as per your convenience


For end to end implementation in analytics life cycle


Life time access for recorded sessions

Students Questions and answers

Any graduate can go for data science, its independent of qualification. Its more over towards logical rather than technical. We will use more conceptual knowledge rather than the technical knowledge in data science. So who so ever have good logical skills can go for data science.

Data science is a process of exploring large, complex and varied data sets to reveal hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations to take calculated risks.

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020. By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.

There is no such prerequisite for data science you might have seen that bachelor degree in statistics and machine learning are mostly data scientists but it is not a prerequisite to learn data science. However if we familiar with the basic concepts of Math and Statistics like Linear Algebra, Calculus, Probability, etc. will help to learn data science.

In both of the modes Palin arrange classes, can go in either of that classroom as well as online.


Everyone have their own opinion towards classroom or online training. In my point of view online classes are far better than classroom session. Online trainings have more much benefits comparative to classroom like we can easily record the session we can access that session whenever we want. In online session we just need a laptop and internet connection can easily access the classroom.

Most of the organizations are working on data analytics platform Like Mckinsey, Delloitte, mercer, TCS, Accenture, Cognizant, IBM, Amex, RBS, UHG, WNS and many more..

An Entry-Level Data Scientist, IT earns an average salary of Rs 579,714 per year. The highest paying skills associated with this job are Machine Learning, Python, and SQL.

Will cover whole process of data science like initially if we want to fetch some data from traditional database we should have the knowledge of SQL. After fetching Data for data mining, data cleaning or data preparation process. After that we import that data into python, after that for predictive analytics will cover basic stats or modelling techniques. will cover some machine learning algorithms. For report presentation n dashboards will use tableau. So data science includes SQL, Python, Predictive Analytics, Machine learning and tableau.

No, One can go for python directly. No need of backgroung in analytics as well as SAS, R or any other statistics tool

Will work on live data set of ecommerce industry, one can take any industry data set. Will decide a goal statement and will follow the flowchart to achieve that

Artificial intelligence is an umbrella term which include technologies like MACHINE LERARNING, DEEP LEARNING, BIG DATA, VIRTUAL AGENTS, SPEECH RECOGINITION to name a few

In any domain we can use data science like agriculture, BFSI, Manufacturing, Healthcare, Oil and Gas, Transportation, Automobiles, Retails and others

Predictive analytics is the branch of the advanced analytics which is used to make predictions about future events. Predictive analytics uses many techniques from data mining, Statistics, Modelling, Machine Learningand artificial intelligence to analyze current data to make predictions about future

Pictorial Representation of data with an aim to allow more room for understanding is what data visualization is all about.

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