Data Science Training In Chandigarh

Data Science Training In Chandigarh

Overview:

Learn Data Science with the best training institute in Chandigarh- Rohar Academy

This Data Science with Python course empowers you to ace Data Science Analytics using Python. In this Data Science with Python training program, you will figure out how to work on Python’s well known data science libraries and jupyter notebook. By the end of this course, you will be  competent enough to fetch data online, preprocess it, perform statistical operations and conduct data visualizations. Further, you will gain insights into data analysis, data visualization, web scraping, machine learning, and natural language processing. Rohar Academy is your one-stop-destination towards building a great career as a Data Science expert. So, register now with Rohar Academy for the best Data Science with Python training course in Chandigarh.

Course Objectives

The main objectives of the course are:

  • To impart in depth understanding of the key concepts in data science and business analytics, including data mining, machine learning, data visualization, predictive modeling, and statistics
  • To help the participants gain hands-on experience in problem analysis and decision-making
  • To help the participants gain hands-on experience in statistical programming languages and big data frameworks

Prerequisite:

No prerequisite required. Anyone who wants to learn Data Science can enroll in this course. Although, basic knowledge in mathematics and python would be a plus point

Target Audience

Anyone from mathematics, science, or engineering background, who wants to enhance their skills or is looking for a career in data science 

Learning Outcomes

  • Basics of python Language.
  • Scientific libraries in Python – NumPy, SciPy, Matplotlib and Pandas
  • Data Visualization
  • Learn Scikit-learn and Machine Learning
  • Deep Learning

MAIN FEATURES

  • The participants will become competent enough to apply quantitative modeling and data analysis  solutions to real world business problems
  • They will be able to recognize and analyze issues like data security, integrity, and privacy
  • The participants will also have improved decision making skills
  • Last but not the least, the participants will be able to apply fundamentals of Data Science to solve business related problems

Why learn Data Science with Python?

Lately, Data Science has become the most popular buzzword in the IT industry, and Data Science has become one of the most sought after career paths among IT experts. Today, an ever increasing number of organizations, small scale or large enterprises, have realized how significant Data Science is, and are actualizing its capacities to effectively scale up their business higher than ever. Infact, data scientists are paid quite handsome salary packages as compared to their peers. Want to take charge and be in the driver’s seat for your job hunt? Enroll now with Rohar Academy, the best Data Science with Python training institute in Chandigarh, and achieve great heights in your career.

Module 1: Python for Data Science

1
Python Essentials
2
Overview of Python- Starting with Python
3
Why Python for data science?
4
Anaconda vs. python
5
Introduction to installation of Python
6
Introduction to Python IDE’s (Jupyter, python)
7
Concept of Packages – Important packages
8
NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc.
9
Installing & loading Packages & Name Spaces
10
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
11
List and Dictionary Comprehensions
12
Variable & Value Labels – Date & Time Values
13
Basic Operations – Mathematical/string/date
14
Control flow & conditional statements
15
Debugging & Code profiling
16
Python Built-in Functions (Text, numeric, date, utility functions)
17
User defined functions – Lambda functions
18
Concept of apply functions
19
Python – Objects – OOPs concepts
20
How to create & call class and modules?

Operations with NumPy And Pandas

1
What is NumPy?
2
Overview of functions & methods in NumPy
3
Data structures in NumPy
4
Creating arrays and initializing
5
Reading arrays from files
6
Special initializing functions
7
Slicing and indexing
8
Reshaping arrays
9
Combining arrays
10
NumPy Math
11
What is pandas, its functions & methods
12
Pandas Data Structures (Series & Data Frames)
13
Creating Data Structures (Data import – reading into pandas)

Data Analysis and Visualizations

1
Pandas, its functions & methods
2
Pandas Data Structures (Series & Data Frames)
3
Creating Data Structures (Data import – reading into pandas)
4
Pandas Data Structures (Series & Data Frames)
5
Creating Data Structures (Data import – reading into pandas)

Module 2: Mathematics Involved in the Data Science

1
Introduction to basic statistics.
2
Introduction to analytics and Data science.
3
Introduction to the mathematical fundamentals.
4
Linear Algebra
5
Descriptive Statistics
6
Inferential Statistics
7
Hypothesis Testing
8
Probability Basics
9
Basics of Calculus and Optimization Functions
10
Basic Linear Regression Intuition with SGD

Module 3: Business analytics Python

Module 4: Databases SQL

Advance SQL

Apache and Hadoop

1
Introduction to Database
2
Introduction to MySQL and NoSQL
3
DDL v/s DML v/s DCL v/s TCL
4
Basics of Database
5
Basic and Advance Queries
6
Filtering Data using WHERE and ORDER BY Clause
7
Displaying Data from Multiple tables
8
Grouping Data and Computing Aggregates using GROUP BY & HAVING Clause
9
Subqueries and Nested queries in SQL
10
Basics to core apache and Hadoop

Module 5: Machine Learning Algorithms.

1
Linear Regression Models
2
Correlational vs Regressions
3
Geometrical Representations of the models.
4
Using Seaborn for model visualizations.
5
Multiple Regression models
6
Adjusted R-Square
7
Advance Statical methods with sklearn.
8
Logistic Regressions.
9
Building of the model of Logistic Regressions
10
Variance and Bias
11
Underfitting and Overfitting
12
Testing the model
13
Cluster Analysis- K-means clustering
14
Pros and cons of clustering
15
Relationship between clustering and regressions.
16
Other type of clustering.

Module 6: Deep Learning Module.

1
Deep Learning
2
Basics & Need
3
Advantages over Machine learning
4
The Brain vs Neuron
5
TensorFlow 2.x Platform:
6
TensorFlow Introduction
7
TensorFlow Basic Concepts
8
Installation and Basic Operations in TF 2.X
9
TF 2.0 Eager Mode
10
TensorFlow 2.X – Keras
11
Tensors
12
Computation Graph
13
Installing TensorFlow
14
TensorFlow training
15
Prepare Data
16
Tensor types
17
Loss and Optimization
18
Running TensorFlow programs

Neural Networks:

1
Structure of Neural Networks
2
Neural Network – Core Concepts
3
Feed Forward Algorithm
4
Backpropagation
5
Building Neural Network from scratch using NumPy
6
Algorithms:
7
Convolution Neural Network
8
Convolutional Operation
9
ReLU Layers
10
What is Pooling vs Flattening
11
Full Connection
12
SoftMax vs Cross Entropy
13
R-CNN, Fast, Faster R-CNN
14
Disadvantage of the CNN with respect to the datasets.
15
RNN
16
Recurrent neural networks RNN
17
LSTMs understanding LSTMs
18
long short-term memory neural networks lstm in python

Introduction to Artificial Neural Networks

1
The Detailed ANN
2
The Activation Functions
3
How do ANNs work & learn
4
Gradient Descent
5
Stochastic Gradient Descent
6
Backpropagation
7
Understand limitations of a Single Perceptron
8
Understand Neural Networks in Detail
9
Illustrate Multi-Layer Perceptron
10
Backpropagation – Learning Algorithm

Module 7: Live Projects and Visualization in Tableau

1
Case Studies
2
Live Datasets training
3
Visualizations in tools
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Duration: 1 Year (365 Hrs)
Lectures: 123
Level: Advanced

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Monday9:30 am - 6.00 pm
Tuesday9:30 am - 6.00 pm
Wednesday9:30 am - 6.00 pm
Thursday9:30 am - 6.00 pm
Friday9:30 am - 6.00 pm
Saturday9:30 am - 6.00 pm
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Data Science Training In Chandigarh