Data Science

Data Science is one of the most sought-after skills today. From analyzing large datasets to building predictive models, Data Science empowers organizations to make informed decisions and unlock new opportunities. With a strong focus on hands-on learning, our course will prepare you to tackle real-world challenges and become a successful Data Scientist.

Course Highlights

Industry-Relevant Curriculum: Learn the most up-to-date tools, techniques, and frameworks used in Data Science.
Hands-On Projects: Work on real-world datasets to develop practical skills and build an impressive portfolio.
Expert Instructors: Gain insights from experienced Data Science professionals who are leaders in the industry.
Placement Assistance: Benefit from resume building, Real World Project, and job referrals to kickstart your Data Science career.

Our Data Science training program covers a broad spectrum of topics, ensuring a deep understanding of both the theoretical and practical aspects of Data Science.

1. Introduction to Data Science

Overview of Data Science and its applications
The Data Science lifecycle
Roles and responsibilities of a Data Scientist
Setting up the Data Science environment

2. Python for Data Science

Python fundamentals and libraries
Data manipulation with Pandas
Data visualization with Matplotlib and Seaborn
Working with NumPy for numerical operations

3. Statistics and Probability

Descriptive statistics
Probability theory and distributions
Hypothesis testing
Statistical inference

4. Data Wrangling and Preprocessing

Data cleaning and handling missing values
Data transformation and feature scaling
Handling outliers and anomalies
Exploratory data analysis (EDA)

5. Data Visualization

Principles of effective data visualization
Creating insightful charts and graphs
Advanced visualization with Plotly and Tableau
Communicating data insights effectively

6. Machine Learning with Python

Introduction to Machine Learning algorithms
Supervised vs. unsupervised learning
Linear Regression and Logistic Regression
Decision Trees, Random Forest, and Gradient Boosting
K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)

7. Deep Learning and Neural Networks

Introduction to Deep Learning
Understanding neural networks and their architecture
Building neural networks using TensorFlow and Keras
Convolutional Neural Networks (CNN) for image data
Recurrent Neural Networks (RNN) for sequence data

8. Natural Language Processing (NLP)

Introduction to NLP and text analytics
Text preprocessing and tokenization
Sentiment analysis and text classification
Building chatbots and language models

9. Big Data and Data Engineering

Introduction to Big Data and Hadoop
Working with Apache Spark for large-scale data processing
Data pipelines and ETL processes
Integration with cloud platforms (AWS, Azure, GCP)

This course is ideal for:

Aspiring Data Scientists looking to start a career in this lucrative field.
IT Professionals and Developers seeking to transition into Data Science roles.
Business Analysts aiming to enhance their analytical skills.
Professionals wanting to upgrade their skill set and stay competitive in the job market.
Students and Freshers interested in building a strong foundation in Data Science.

Career Opportunities

Completing the Java Backend Development Training opens doors to a variety of roles, such as:

Backend Developer
Java Developer
API Developer
Microservices Developer
Software Engineer

Prerequisites

Any Degree pursuing or passed out
Computer knowledge is not necessary
Laptop or Desktop with internet is necessary