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India's Most Trusted Python Wth Data Science in Chennai

First Time In India - Job Oriented
Intensive Training & Internship Program

14,000 Students Placed
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14 Years of Students Trust​

Overview

The course objectives of a data science program typically aim to equip students or participants with a comprehensive understanding of the principles, tools, and techniques used in handling, analyzing, and deriving insights from data. These objectives may vary depending on the specific course, institution, or level of the program (introductory, intermediate, or advanced). Here are some common course objectives for a data science curriculum:

Course Objectives:

The course objectives of a data science program typically aim to equip students or participants with a comprehensive understanding of the principles, tools, and techniques used in handling, analyzing, and deriving insights from data. These objectives may vary depending on the specific course, institution, or level of the program (introductory, intermediate, or advanced). Here are some common course

  1. Learn the basics of data types, structures, and formats.
  2. Grasp foundational concepts in data collection, cleaning, and preprocessing.
  • Gain proficiency in statistical analysis methods.
  • Understand probability distributions, hypothesis testing, and regression analysis.
  • Explore exploratory data analysis (EDA) methods.
  • Learn supervised and unsupervised machine learning algorithms for classification, regression, clustering, etc.
  • Understand feature engineering, model evaluation, and validation techniques.
  • Develop skills in creating effective visualizations to communicate insights.
  • Understand best practices for data presentation and storytelling through visualizations.
  • Familiarize with tools and frameworks for handling big data (e.g., Hadoop, Spark).
  • Learn techniques for distributed computing and data processing.
  • Introduce concepts and techniques for analyzing textual data.
  • Understand sentiment analysis, text classification, and information retrieval.
  • Explore advanced techniques like deep learning and neural networks for handling complex data types (e.g., images, audio, sequences).
  • Apply learned concepts and techniques to real-world datasets.
  • Work on projects to solve business problems or explore specific research questions.

Discuss ethical considerations related to data collection, privacy, bias, and transparency in data-driven decision-making.

  • evelop the ability to communicate technical findings to non-technical stakeholders effectively.
  • Encourage the habit of staying updated with the latest trends, tools, and methodologies in data science.

What Skills You Will Learn

nump
NumPy​
Pandas
Matplotlib
seaborn
Seaborn
Scikit-learn
SciPy

Who is this Demo for?

Designed for Everyone Looking for a Software Job

Graduates & Final Year students of all branches

Mech, Civil, Chem, ECE, EEE, CS/IT etc.

Non-IT Professionals looking to shift to IT

Get higher salaries you deserve for your capabilities.

Those worrying about low CGPA/backlogs

Your academic performance will not hold you back.

IT Professionals looking for Product Development roles

Switch from support/testing roles to high-paid developer roles.

Why attend the Demo?

Class rooms

c4

Offered Programs

JOB ORIENTED INTENSIVE PROGRAM (JOIP)​

1. Any Graduate

2. By Realtime Experts

3. Weekly Mock Interviews

4. Resume Preparation

5. Up to 3 Live Projects

6. 4 – 6 Hrs Daily Training

7. Offline / Online

8. Training Completion Certificate

9. Backup videos for 4mnths

JOB ORIENTED INTERNSHIP PROGRAM (JOIP)

1. Any Graduate

2. Must attend Mock Test

3. By Realtime Experts

4. practical in Company 

Environment (Hitech City

5. Live Project work assigned 

by IT Staff (Hitech City)

6. Preferred Offline / Online

7. Internship Completion Certificate

8. 6 – 8 Hrs Daily

9. Backup videos for 12 months

After Completing this course, you will be able to:

  1. What is Python’s role in data science?
    Python is a versatile programming language widely used in data science due to its rich ecosystem of libraries (like NumPy, Pandas, Scikit-learn) that facilitate data manipulation, analysis, visualization, and machine learning tasks.

  2. Which Python libraries are essential for data science?
    Essential libraries include NumPy and Pandas for data manipulation, Matplotlib and Seaborn for visualization, Scikit-learn for machine learning, TensorFlow or PyTorch for deep learning, and Jupyter Notebooks for interactive computing.

  3. How do I handle missing data in Python? 
    Pandas provides methods like isnull(), dropna(), and fillna() to identify, remove, or replace missing values in datasets effectively.

  4. What are the differences between NumPy and Pandas?
    NumPy focuses on numerical computing with multi-dimensional arrays, while Pandas is built on top of NumPy and specializes in data manipulation and analysis with Series and DataFrame structures.

  5. How do I visualize data in Python?
    Matplotlib and Seaborn are popular libraries for creating various types of visualizations like line plots, histograms, scatterplots, and more. They offer customizable options for visualizing data effectively.

  6. What machine learning algorithms can I implement using Scikit-learn?
    Scikit-learn provides implementations for a wide range of machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), clustering algorithms, etc.

  7. How can I start learning deep learning in Python?
    Beginners can start with TensorFlow or PyTorch tutorials available online. Both frameworks offer comprehensive documentation and tutorials for building neural networks and deep learning models.

  8. How do I optimize the performance of my Python code for data processing?
    Techniques like vectorization (using NumPy arrays), avoiding unnecessary loops, utilizing appropriate data structures, and using optimized library functions can significantly enhance code performance.

  9. What are some real-world applications of Python in data science?
    Python is used in various fields such as finance (algorithmic trading, risk assessment), healthcare (disease prediction, image analysis), marketing (customer segmentation, recommendation systems), and more.

  10. How do I get practical experience in Python for data science?
    Practice on real datasets, work on projects, participate in online coding platforms, take part in Kaggle competitions, and contribute to open-source projects to gain hands-on experience.

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FAQ's

Python is a versatile programming language widely used in data science due to its rich ecosystem of libraries (like NumPy, Pandas, Scikit-learn) that facilitate data manipulation, analysis, visualization, and machine learning tasks.

Essential libraries include NumPy and Pandas for data manipulation, Matplotlib and Seaborn for visualization, Scikit-learn for machine learning, TensorFlow or PyTorch for deep learning, and Jupyter Notebooks for interactive computing.

Pandas provides methods like isnull(), dropna(), and fillna() to identify, remove, or replace missing values in datasets effectively.

NumPy focuses on numerical computing with multi-dimensional arrays, while Pandas is built on top of NumPy and specializes in data manipulation and analysis with Series and DataFrame structures.

Matplotlib and Seaborn are popular libraries for creating various types of visualizations like line plots, histograms, scatterplots, and more. They offer customizable options for visualizing data effectively.

Scikit-learn provides implementations for a wide range of machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), clustering algorithms, etc.

Beginners can start with TensorFlow or PyTorch tutorials available online. Both frameworks offer comprehensive documentation and tutorials for building neural networks and deep learning models.

Techniques like vectorization (using NumPy arrays), avoiding unnecessary loops, utilizing appropriate data structures, and using optimized library functions can significantly enhance code performance.

Python is used in various fields such as finance (algorithmic trading, risk assessment), healthcare (disease prediction, image analysis), marketing (customer segmentation, recommendation systems), and more.

Practice on real datasets, work on projects, participate in online coding platforms, take part in Kaggle competitions, and contribute to open-source projects to gain hands-on experience.

WHAT DEMO ATTENDEES HAVE TO SAY