OUR TRAINING COURSES

Machine Learning Using Python 4.67/5 (12)

Review (15)

39,500.00

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.

24 Sessions
192 Hours



Skills you will master

EDA
Python
Statistics
Linear regression
Logistic Regression
Predictive Modeling
Machine Learning
KNN
Random Forest
Data wrangling
Numpy pandas and matplotlib
GIT
Classification
Unsupervised Learning
Deep Learning
Artificial Inteligence
Neural Networks
Data Visualization
Tableau Dashboards

UPCOMING BATCHES

22
Dec
Weekends
03:00 PM - 07:00 PM
05
jan
Weekends
10:00 AM - 01:00 PM
Mob : +91-9810600764
Address : M8, Lower ground Floor, Sector 14 OLD DLF Gurgaon 122001
Email : info@palin.co.in

39,500.00

Category:
About The Program

Data Science Training Using Python

The data science is designed by Data Scientists keeping in mind of upcoming industry requirement and latest trend to fulfill the pool of manpower. It enhance your technical, conceptual as well as the logical approach towards data, which helps you to understand the data as well as to get insights of it. This program gives you the high quality content to analyze after collection of data from different sources like sensors, web, mobiles and social media using various statistical and machine learning algorithms. Data science training with Python is a Flight to a better career path

 

During this training participants would be able to use data mining, cleaning n preparation techniques using excel and then slowly switch over the most demanding platforms R and SAS will be able to use various data analytics concepts quantitative and qualitative research consist in respect to data collection, data sample, data analysis…
Data Science training with Python

 

During this training participants

During this training participants

After the completion of the course you will be able to crack the interviews with most of the renowned organizations like Accenture, IBM, Dunnhumby, EXL, Bank of America, American Express, Fractal Analytics, Cognizant and many more. It’s a 100 hours instructor Led training and unlimited access of training material, recordings, assignments, projects. Data Science Training with Python in Gurgaon

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Career Advisor
Vinit Kumar, Consultant
Palin Delivers real world relevance with activities and assignments that helps students build critical thinking and analytic skills that will transfer to other courses and professional life.
Program Description
Data science master program is made keeping in mind the profile that most of the corporate are asking for a data scientist. It includes advance excel for data mining purpose and sql for communication with database. SAS and R are platforms to use for analytics using modeling techniques which are used for cluster analysis, time series analysis, market basket analysis and regression. Machine learning algorithm are also added to the course keeping in mind the requirement of industry to include artificial intelligence into analytics. For data visualization and reporting we use tableau. To work with different domains like finance, marketing, ecommerce, banking, insurance, aviation and even games also.
Program Preview

Curriculum
Click Here to Preview

   Session 01- Introduction to data science
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- 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)

   Session 02 - Data Science in different domaims
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- 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

   Session 03 - Exploratory Analysis
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- 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

   Session 04 - Python Ecosystem
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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

   Session 05 - Concepts of Python
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Python Basics
- 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

   Session 06 - Working with Python
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Programming with Python
- 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

   Session 07 - Data Sourcing and Data mining
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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

   Session 08 - Libraries and data visualization
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Numpy, Pandas & Matplotlib
- 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

   Session 09-10 GIT and basics of statistics
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GIT (Bonus Lecture for Aspiring Programmers)
- 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!
STATISTICS
Statistics for Business Analytics
- 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

   Session 11 Intoduction to machine learning
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MACHINE LEARNING
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

   Session 12-13 Regression modeling
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Regression Modeling
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression

   Session 14-15 Data Classification
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Classification
- Logistic Regression
- K-Nearest Neighbours (K-NN)
- Support Vector Machines
- Kernel SVM
- Naive Bayes Classifier
- Decision Tree Classification
- Random Forest Classification

   Session 16-17 - Unsupervised Learning
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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

   Session 18 Intoduction to Deep Learning
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XGBoost in Python (Bonus Lecture)
Deep Learning
- Introduction to Deep Learning
- The Human Brain and how it works

   Session 19-20-21 Live Project
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Project: Predicting if the customer will accept the shipment or return it

   Session 22 - Data Visualization with Tableau
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- 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

   Session 23-24 Working with Tableau Dashboard
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Project : A dashboard should be created that would contain: 
- 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
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

Read More
Program Highlights
Instructor-led
Live interactive and classroom training will include the practical approach and assessments on regular basis.
Live Case Study
Domain Specific Live interactive case study using industry specific data, problem statements, solution architecture.
Assignments
Assignments which helps in conceptual understanding, Prepared for interview training & techniques.
Lifetime Access
Class recording, Study material ppt’s, pdf, assignments, datasets, case studies can be accessed through out the lifetime.
24 X 7 Support
We will support If any concern is raised related to the training, assignments, projects, case studies & interview questions.
Certification
After successful completion of the training and case study Palin will certify you as Palin certified Data Scientist.
Career Counselor
Avail career guidance and Professional guidance for resume building, unlimited opportunities and interviews.
FAQ's
   Who can go for Data Science?
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.
   What is 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.
   What is future of data scientists?
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.
   Pre-requisite for data science?
There is no such prerequisite for data science you might have seen that bachelor's 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.
   Is this a classroom training or online?
In both of the modes Palin arrange classes, can go in either of that classroom as well as online.
   What are topics we cover in data science using python?
Data science introduction, diffrent data sources like RDBMS, NOSQL, MACHINE LESRNING, PREDICTIVE ANALYTICS, DEEP LEARNING, SUPERVISED & UNSUPERVISED, KNN CLASSIFICATIONS and one LIVE PROJECT
   Which one is better classroom or 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.
   After the course for which companies I can apply for?
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..
   What are pay packages for fresher in Data Science?
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.
   What is the process for data science?
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.
   Is there any Pre-requisite for Python?
No, One can go for python directly. No need of backgroung in analytics as well as SAS, R or any other statistics tool
   Which type of project will cover in this training?
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
   What is artificial intelligence ?
Artificial intelligence is an umbrella term which include technologies like MACHINE LERARNING, DEEP LEARNING, BIG DATA, VIRTUAL AGENTS, SPEECH RECOGINITION to name a few
   What are the different domains ?
In any domain we can use data science like agriculture, BFSI, Manufacturing, Healthcare, Oil and Gas, Transportation, Automobiles, Retails and others
   What is perdictive analytics ?
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
   Whu should we lear data visualization ?
Pictorial Representation of data with an aim to allow more room for understanding is what data visualization is all about.
Reviews

Reviews

    Nice faculty.. Go for data science course worth it. Value for money and nature of staff is very nice. Kudos!

    It’s an awesome institute for all those who want to make & grow their career in Data Science & Analytics. Very experienced professional trainers who enhance your knowledge & skills with great teaching methodologies!! #Hats off Palin#
    Like

    It is a very well established analytics coaching institute.
    You can get customised sessions as well. Trainers are very well equipped with tools they use while teaching and have deep knowledge.

    Very professional staff and faculty. Quality of training is excellent and meets industry standards. They are also helpful in getting placements in relevant industry leaders in Data Sciences.

    Experience was great,with regards to knowledge,what we learnt we are applying. The knowledge we gained was in depth.

    It has been amazing learning experience.Working on live project with high quality trainers.Now we got to know where we can use GIT, Regression modeling, linear, logistic everything….Thankyou Palin for enhance my knowledge.

    It has been rightly quoted “Data is a precious thing and will last longer than the system themselves.” I really appreciate “Palin Analytics” such a fantastic institute, with good and dedicated staff.

    Palin is the place to be to give a kickass start to ur career in Data Analytics. Training they provide is valuable and best in the context. They value the core knowledge of students and help them grow. A big thankx to all the trainers

    Hi All, Those who are looking for an institute to learn Data Analytics,SAS,R. Let me tell you Palin is the best institute in Gurgaon.
    After visiting so many institutes I found that it is the best to learn and invest your money and time. The Faculty Sourav is very good. He clears all doubts with no hurry. Every students get time for their query resolution. Even the other faculty Like. Vineet, Nitesh helps alot. Especially the person i met there is Jaivinder who guided me for my career path and is very helping. Its a good learning for me. I would suggest every one dont go other institute to waste your time/money. If you really want to learn that is the place. Happy Learning and Never Give up in life for your family…..

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

    Thank you Palin for enhancing my skills in Data science. *Highly Recommended*

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

    The best institute for Data Science training. I had chosen the predictive analytics course and the training was excellent.

    Excellent faculties for all courses.there is so much to learn.recommending you to recruit yourself to get something more valuable than money.

    Training was highly knowledgeable. It helped me to enhance my skills and achieve my career goals.

    Great learning environment. Trainers are industry professionals and have indepth knowledge.

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