Enrolling in this Python Data Science course in Cochin will help you become a proficient Data Scientist with Python skills, such as NumPy, Lambda functions, SciPy, etc. Our SMEs, with over 12 years of experience, will be there to help you 24/7. This course also includes case studies that will test your skills and help you better understand the concepts. So, enroll for the best Data Science with Python Course in Cochin created by experts.
Once you start learning in this Data Science with Python training in Cochin, you will cover all Data Science fundamentals, along with crucial concepts such as linear regression, Scikit-Learn, Machine Learning, data manipulation, analysis, etc.
In this Data Science with Python Course in Cochin online, you will learn about
You will also be required to work on Data Science projects with wide practical applications to complete this course.
The following facts prove why joining this Python for Data Science course in Cochin is the right decision for your career:
Intellipaat Data Science with Python training in Cochin is suitable for
Register today and start your career as a Data Scientist with Python skills!
There are no mandatory prerequisites for joining this Data Science with Python training in Cochin. Basic knowledge in programming will, however, help you through your learning.
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1.1 What is Data Science, what does a data scientist do
1.2 Various examples of Data Science in the industries
1.3 How Python is deployed for Data Science applications
1.4 Various steps in Data Science process like data wrangling, data exploration and selecting the model.
1.5 Introduction to Python programming language
1.6 Important Python features, how is Python different from other programming languages
1.7 Python installation, Anaconda Python distribution for Windows, Linux and Mac
1.8 How to run a sample Python script, Python IDE working mechanism
1.9 Running some Python basic commands
1.10 Python variables, data types and keywords.
Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac
2.1 Introduction to a basic construct in Python
2.2 Understanding indentation like tabs and spaces
2.3 Python built-in data types
2.4 Basic operators in Python
2.5 Loop and control statements like break, if, for, continue, else, range() and more.
Hands-on Exercise –
1.Write your first Python program
2. Write a Python function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. Create an object and write a for loop to print all odd numbers
3.1 Central Tendency
3.2 Variabiltiy
3.3 Hypothesis Testing
3.4 Anova
3.5 Correlation
3.6 Regression
3.7 Probability Definitions and Notation
3.8 Joint Probabilities
3.9 The Sum Rule, Conditional Probability, and the Product Rule
3.10 Baye’s Theorem
Hands-on Exercise –
1. We will analyze both categorical data and quantitative data
2. Focusing on specific case studies to help solidify the week’s statistical concepts
4.1 Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction
4.2 What are access modifiers, instances, class members
4.3 Classes and objects
4.4 Function parameter and return type functions
4.5 Lambda expressions.
Hands-on Exercise –
1. Writing a Python program and incorporating the OOP concepts
5.1 Introduction to mathematical computing in Python
5.2 What are arrays and matrices, array indexing, array math, Inspecting a numpy array, Numpy array manipulation
Hands-on Exercise –
1. How to import numpy module
2. Creating array using ND-array
3. Calculating standard deviation on array of numbers and calculating correlation between two variables.
6.1 Introduction to scipy, building on top of numpy
6.2 What are the characteristics of scipy
6.3 Various subpackages for scipy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more, Bayes Theorem with scipy.
Hands-on Exercise:
1. Importing of scipy
2. Applying the Bayes theorem on the given dataset.
7.1 What is a data Manipulation. Using Pandas library
7.2 Numpy dependency of Pandas library
7.3 Series object in pandas
7.4 Dataframe in Pandas
7.5 Loading and handling data with Pandas
7.6 How to merge data objects
7.7 Concatenation and various types of joins on data objects, exploring dataset
Hands-on Exercise –
1. Doing data manipulation with Pandas by handling tabular datasets that includes variable types like float, integer, double and others.
2. Cleaning dataset, Manipulating dataset, Visualizing dataset
8.1 Introduction to Matplotlib
8.2 Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more
8.3 Matplotlib API
Hands-on Exercise –
1. Deploying Matplotlib for creating pie, scatter, line and histogram.
2. Subplots and Pandas built-in data visualization.
9.1 Revision of topics in Python (Pandas, Matplotlib, numpy, scikit-Learn)
9.2 Introduction to machine learning
9.3 Need of Machine learning
9.4 Types of machine learning and workflow of Machine Learning
9.5 Uses Cases in Machine Learning, its various arlogrithms
9.6 What is supervised learning
9.7 What is Unsupervised Learning
Hands-on Exercise –
1. Demo on ML algorithms
10.1 What is linear regression
10.2 Step by step calculation of Linear Regression
10.3 Linear regression in Python
10.4 Logistic Regression
10.5 What is classification
10.6 Decision Tree, Confusion Matrix, Random Forest, Naïve Bayes classifier (Self paced), Support Vector Machine(self paced), xgboost(self paced)
Hands-on Exercise – Using Python library Scikit-Learn for coming up with Random Forest algorithm to implement supervised learning.
11.1 Introduction to unsupervised learning
11.2 Use cases of unsupervised learning
11.3 What is clustering
11.4 Types of clustering(self-paced)-Exclusive clustering, Overlapping Clustering, Hierarchical Clustering(self-paced)
11.5 What is K-means clustering
11.6 Step by step calculation of k-means algorithm
11.7 Association Rule Mining(self-paced), Market Basket Analysis(self-paced), Measures in association rule mining(self-paced)-support, confidence, lift
11.8 Apriori Algorithm
Hands-on Exercise –
1. Setting up the Jupyter notebook environment
2. Loading of a dataset in Jupyter
3. Algorithms in Scikit-Learn package for performing Machine Learning techniques and training a model to search a grid.
4. Practice on k-means using Scikit
5. Practice on Apriori
12.1 Introduction to pyspark
12.2 Who uses pyspark, need of spark with python
12.3 Pyspark installation
12.4 Pyspark fundamentals
12.5 Advantage over mapreduce, pyspark
12.6 Use-cases pyspark and demo.
Hands-on Exercise:
1. Demonstrating Loops and Conditional Statements
2. Tuple – related operations, properties, list, etc.
3. List – operations, related properties
4. Set – properties, associated operations, dictionary – operations, related properties.
13.1 Introduction to Dimensionality
13.2 Why Dimensionality Reduction
13.3 PCA
13.4 Factor Analysis
13.5 LDA
Hands-on Exercise –
Practice Dimensionality reduction Techniques : PCA, Factor Analysis, t-SNE, Random Forest, Forward and Backward feature
14.1 White Noise
14.2 AR model
14.3 MA model
14.4 ARMA model
14.5 ARIMA model
14.6 Stationarity
14.7 ACF & PACF
Hands-on Exercise –
1. Create AR model
2. Create MA model
3. Create ARMA model
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Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500 companies.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
This Intellipaat Python for Data Science training will give you hands-on experience in mastering one of the best programming languages that is Python. In this online Python for Data Science course, you will learn about the basic and advanced concepts of Python including MapReduce in Python, Machine Learning, Hadoop streaming and also Python packages like Scikit and Scipy. You will be awarded the Intellipaat Course Completion Certificate after successfully completing the training course.
As part of this online Data Science with Python course, you will be working on real-time Python projects that have high relevance in the corporate world and step-by-step assignments, and the curriculum is designed by industry experts. Upon the completion of the Python for Data Science certification, you can apply for some of the best jobs in top MNCs around the world at top salaries. Intellipaat offers lifetime access to videos, course materials, 24/7 support and course material upgrading to the latest version at no extra fees. Hence, it is clearly a one-time investment.
This Intellipaat Python for Data Science training will give you hands-on experience in mastering one of the best programming languages that is Python. In this online Python for Data Science course, you will learn about the basic and advanced concepts of Python including MapReduce in Python, Machine Learning, Hadoop streaming and also Python packages like Scikit and Scipy. You will be awarded the Intellipaat Course Completion Certificate after successfully completing the training course.
As part of this online Data Science with Python course, you will be working on real-time Python projects that have high relevance in the corporate world and step-by-step assignments, and the curriculum is designed by industry experts. Upon the completion of the Python for Data Science certification, you can apply for some of the best jobs in top MNCs around the world at top salaries. Intellipaat offers lifetime access to videos, course materials, 24/7 support and course material upgrading to the latest version at no extra fees. Hence, it is clearly a one-time investment.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.
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