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Curriculum in Data Science
Data science is preferred by more than 55% of developers. In the tech industry, data science is the most well-liked and in-demand programming language.
- Introduction to Data Science
- Significance of Data Science
- 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
- Introduction to Data Visualisation
- Various kinds of graphs, Graphics grammar
- Ggplot2 package
- Multivariant analysis by using geom_boxplot
- Univariant analysis
- Histogram, barplot, multivariate distribution, and density plot
- Bar plots for the categorical variables through geop_bar() and the theme() layer
- Statistics Importance
- 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 Learning, Classification in Supervised Learning
- Linear Regression and mathematical concepts related to linear regression
- Classification Algorithms, Ensemble Learning techniques
- Logistic Regression Introduction
- Logistic vs Linear Regression, Poisson Regression
- Bivariate Logistic Regression, math related to logistic regression
- Multivariate Logistic Regression
- Building Logistic Models
- False and true positive rate
- Real-time applications of Logistic Regression
- Classification Techniques
- Decision Tree Induction Algorithm
- Implementation of Random Forest in R
- Differences between classification tree and regression tree
- Naive Bayes, SVM
- Entropy, Gini Index, Information Gain
- Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
- Unsupervised learning, Clustering algorithm, K-means clustering algorithm
- K-means theoretical concepts, k-means process flow, and K-means implementation
- Implementing Historical Clustering in R
- PCA(Principal Component Analysis) Implementation in R
- DoS/DDoS Concepts
- Botnets
- DoS/DDoS Attack Techniques
- DDoS Case Study
- DoS/DDoS Countermeasures
- Natural language processing and Text mining basics
- Significance and use-cases of text mining
- NPL working with text mining, Language Toolkit(NLTK)
- Text Mining: pre-processing, text-classification and cleaning
- Numpy Basics
- Numpy Mathematical Functions
- Probability Basics and Notation
- Correlation and Regression
- Joint Probabilities
- Bayes Theorem
- Conditional Probability, sum rule, and product rule
- Scipy Introduction and characteristics
- Integrate, Cluster, Signal, Fftpack, and Bayes Theorem
- Pyspark basics
- Uses and Need of pyspark
- Pyspark installation
- Advantages of pyspark over MapReduce
- Pyspark applications
- Machine Learning effect on Artificial Intelligence
- Deep Learning Basics, Working of Deep Learning
- Regression and Classification in the Supervised Learning
- Association and Clustering in unsupervised learning
- Basics of Artificial Intelligence and Neural Networks
- Supervised Learning in Neural Networks, multi-layer network
- Deep Neural Networks, Convolutional Neural Networks
- Reinforcement Learning
- Recurrent Neural Networks, Deep learning graphics processing unit
- Deep Learning Applications, Time series modeling
- Tensorflow Basics and Tensorflow open-source libraries
- Deep Learning Models and Tensor Processing Unit(TPU)
- Graph Visualisation, keras
- Keras neural-network
- Define and Composing multi-complex output models through Keras
- Batch normalization, Functional and Sequential composition
- Implementing Keras with tensorboard
- Implementing neural networks through TensorFlow API
- Basics of Autoencoders and rbm
- Implementing RBM for the deep neural networks
- Autoencoders features and applications
- Big Data and Hadoop Basics
- Hadoop Architecture, HDFS
- MapReduce Framework and Pig
- Hive and HBase
- Basics of Scala and Functional Programming
- Kafka basics, Kafka Architecture
- Kafka cluster and Integrating Kafka with Flume
- Introduction to Spark
- Spark RDD Operations, writing spark programs
- Spark Transformations, Spark streaming introduction
- Spark streaming Architecture, Spark Streaming Features
- Structured streaming Architecture, Dstreams, and Spark Graphx
- Data Visualisation Basics
- Data Visualisation Applications
- Tableau Installation and Interface
- Tableau Data Types, Data Preparation
- Tableau Architecture
- Getting Started with Tableau
- Creating sets, Metadata and Data Blending
- Arranging visual and data analytics
- Mapping, Expressions, and Calculations
- Parameters and Tableau prep
- Stories, Dashboards, and Filters
- Graphs, charts
- 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
- SAS Enterprise Guide
- SAS functions and Operators
- SAS Data Sets compilation and creation
- SAS Procedures
- SAS Graphs
- SAS Macros
- PROC SQL
- Advance SAS
- Entering Data
- Logical Functions
- Conditional Formatting
- Validation, Excel formulas
- Data sorting, Data Filtering, Pivot Tables
- Creating charts, Charting techniques
- File and Data security in excel
- VBA macros, VBA IF condition, and VBA loops
- VBA IF condition, For loop
- VBA Debugging and Messaging
