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.

- 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

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

- 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

- 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

- 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

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

- 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

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

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

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

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

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

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

- 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

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.

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..

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.

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

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

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

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

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