Data Science Course
The Data Science course is designed by experts with extensive real-world experience in artificial intelligence, machine learning, deep learning, and a variety of other cutting-edge technologies.
Data Science Course Overview
The course focuses on giving you a thorough understanding of data science, including its applications and usage, R statistical computing, data manipulation, data visualisation, using descriptive and inferential statistics on the data, and much more. Furthermore, through working on actual projects and offering fixes for the issues, you’ll also learn how to do it. In a word, this online training in data science gives you complete confidence to attend interviews and work as a data scientist.
Course Curriculum
Learn the basics of data science and R programming, Importance of Data Science.
- What is Data Science
- Significance of Data Science in today’s world.
- R Programming basics
- Python Introduction
- Indentations in Python
- Python data types and operators
- Python Functions
- Data Structures Overview
- Identifying the Data Structures
- Allocating values to the Data Structures
- Data Manipulation Significance
- Dplyr Package and performing different data manipulation operations.
- Statistics Inference
- Statistics classification, Statistical terminology
- Data types, Probability types, measures of speed, and central tendency
- Covariance and Correlation, Binary and Normal distribution
- Data Sampling, Confidence, and Significance levels
- Hypothesis Test and Parametric testing
- Machine Learning Fundamentals
- Supervised & Unsupervised Learning
- Linear Regression
- Classification Algorithms, Ensemble Learning techniques
- Clustering algorithm, K-means clustering algorithm
- PCA(Principal Component Analysis)
- Logistic Regression Introduction & models
- Logistic vs Linear Regression
- Bivariate Logistic Regression
- Multivariate Logistic Regression
- False and true positive rate, & Real-time applications
- Classification Techniques. Decision Tree Induction Algorithm
- Random Forest
- Naive Bayes, SVM
- Entropy, Gini Index, Information Gain
- Natural language processing and Text mining basics
- Use-cases of text mining
- NPL working with text mining, Language Toolkit (NLTK)
- Text Mining: pre-processing, text-classification, cleaning
- Numpy Basics
- Numpy Mathematical Functions
- Probability Basics and Notation
- Correlation and Regression
- Joint Probabilities
- Bayes Theorem
- Conditional Probability, sum rule, and product rule
- Artificial Intelligence Introduction
- Deep Learning Concepts
- Regression and Classification in the Supervised Learning
- Association and Clustering in unsupervised learning
- Artificial Intelligence and Neural Networks
- Deep Neural Networks, Convolutional Neural Networks
- Reinforcement Learning
- Deep learning graphics processing unit
- Time series modeling
- Tensorflow open-source libraries
- Deep Learning Models and Tensor Processing Unit (TPU)
- Graph Visualisation
- Keras neural-network
- Multi-complex output models through Keras
- Normalization, Functional and Sequential composition
- Implementing Keras with Tensorboard
- Neural Networks through TensorFlow API
- Big Data and Hadoop Basics
- Hadoop Architecture, HDFS
- MapReduce Framework and Pig
- Hive and HBase
- Introduction to Spark
- Spark RDD Operations, writing spark programs.
- Spark Transformation, Spark streaming introduction
- Data Visualisation Basics & Application
- Tableau Installation and Interface
- Tableau Data Types, Data Preparation
- Tableau Architecture
- Creating sets, Metadata and Data Blending.
- Arranging visual and data analytics
- Mapping, Expressions, and Calculations
- Parameters and Tableau prep
- Stories, Dashboards, and Filters
- Integrating Tableau with Hadoop and R
- MongoDB and NoSQL Basics
- MongoDB Installation
- Significance of NoSQL
- CRUD Operations
- Data Modeling and Management
- Data Indexing and Administration
- Data Aggregation Schema
- MongoDB Security
- Collaborating with Unstructured Data
Hands-on Data Science Projects
Our data science training programme aspires to provide high-quality instruction that emphasises practical application of sound underlying knowledge on key issues. Students’ abilities will be enhanced and they will be able to complete real-world projects using the best practises thanks to exposure to use-cases and scenarios from the present industry.
Industry Statistics
Highlights
Sessions Led by Professionals
We provide 10 sessions, per session to 2 hours to complete the course.
Real-Life Case Studies
We have included countless examples and case studies from real life.
Assignments & Projects
You will receive assignments, study material, and projects after each session.
Exclusive Support
You will have access to our expert support in a specific time frame.
Completion Certificate
We will provide you a Course Completion Certificate once you have finished the course.
Job Assistance
Depending on your CTC, experience, and current skills, you will receive 100% job support.