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Data Science Data Science with Python Course

Data Science with Python Course

Course Prerequisites

Please note that this course has the following prerequisites which must be completed before it can be accessed

About This Course

Data science is the analysis of data to identify patterns and trends. Data science with Python is a course that teaches you how to analyze data, visualize data, and use Python for machine learning. In this Data Science with Python course you will learn about various datasets and various methods of analyzing these datasets such as linear regression. This course starts off by teaching the basics of programming in Python. You’ll learn how to work with data structures and variables, write loops, and make decisions using conditionals. Next you’ll learn about data wrangling and how to manipulate data so that it can be used for analysis. Finally you will explore machine learning algorithms like k-nearest neighbors, decision trees, random forests and several other machine learning algorithms before putting your skills into practice.

Data Science with Python Training

Contact for Next batch on Data Science with Python Training.

Course Features

40 Hours of Live sessions

Scheduled Timings

1:1 Classes (Pay Hourly)

Certification guidence

Topic wise Assignments

FREE Demo Session

Hands on Labs

Lifetime access to Class Recordings

Book FREE DEMO Class

Join our Social Learning Group

Learning Objectives

Master the Data Science concepts from scratch.

Learn about the Advanced Data Structure like Numpy, Pandas, Matplotlib.

Learn Machine Learning Algorithms.

Requirements

Laptop/Desktop is required for programming

Target Audience

Both IT and Non IT freshers are eligible.
Working professionals who want to switch to Data Science domain.

Curriculum

55 Lessons40h

Introduction to NumPy, Pandas and Matplotlib

NumPy – arrays and array Operations

Indexing, slicing and iterating

Reading and writing arrays on files

Pandas – data structures & index operations

Reading and Writing data from Excel/CSV formats into Pandas

Matplotlib library – Grids, axes, plots Markers, colours, fonts and styling

Types of plots – bar graphs, pie charts, histograms and Contour plots

Data Manipulation

Pandas

Machine Learning with Python

Supervised Learning – I

Supervised Learning – II

Unsupervised Learning

Association Rules and Recommendation Systems

Reinforcement Learning

Time Series Analysis

Model Selection and Boosting

₹24,000~~₹30,000~~

20% off

Level

Intermediate

Lectures

55 lectures

Subject

Data Science Data Science with Python Course

Data Science with Python Course

Course Prerequisites

About This Course

Data Science with Python Training

Course Features

Book FREE DEMO Class

Join our Social Learning Group

Tags

Learning Objectives

Requirements

Target Audience

Curriculum

Introduction to NumPy, Pandas and Matplotlib

NumPy – arrays and array Operations

Indexing, slicing and iterating

Reading and writing arrays on files

Pandas – data structures & index operations

Reading and Writing data from Excel/CSV formats into Pandas

Matplotlib library – Grids, axes, plots Markers, colours, fonts and styling

Types of plots – bar graphs, pie charts, histograms and Contour plots

Data Manipulation

Introduction to Data Manipulation

Merging and Concatenation of data objects

Types of Joins on data objects

Exploring and Analysing a dataset

Pandas

Pandas Function

Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()

GroupBy operations

Aggregation, Concatenation, Merging and Joining

Machine Learning with Python

Introduction to Machine Learning

Linear regression

Gradient descent

Supervised Learning – I

Classification and its use cases

Introduction to Decision Tree

Algorithm for Decision Tree Induction Creating a Perfect Decision Tree and Confusion Matrix

Random Forest Algorithm

Implementation of Logistic regression Algorithm

Introduction to Dimensionality Reduction

PCA Factor and Analysis Scaling dimensional model

LDA

Supervised Learning – II

Naïve Bayes and How Naïve Bayes works

Implementation of Naïve Bayes Classifier

Support Vector Machine (SVM)

Support Vector Machine illustration

Hyperparameter Optimization

Grid Search vs Random Search

Support Vector Machine for Classification

Unsupervised Learning

Introduction to Clustering

K-means Clustering and K-means algorithm

C-means Clustering and Hierarchical Clustering?

Association Rules and Recommendation Systems

Association Rules and Association Rule Parameters

Calculating Association Rule Parameters

Introduction to Recommendation Engines

Collaborative and Content-Based Filtering

Reinforcement Learning

Introduction to Reinforcement Learning

Elements of Reinforcement Learning

Exploration vs Exploitation dilemma

Epsilon Greedy Algorithm

Markov Decision Process (MDP)

Q values and V values

Q– Learning and α values

Time Series Analysis

Time Series Analysis and Its uses

Components of TSA

White Noise AR, MA, ARMA and ARIMA model

Stationarity

ACF & PACF

Model Selection and Boosting

Introduction to Model Selection and Cross-Validation

Introduction to Boosting and Boosting Algorithms work

Types of Boosting Algorithms and Adaptive Boosting

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