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Curriculum in Data Science
Data Science Basics
Python Fundamentals
Data Structures and Data Manipulation
Data visualization
Statistics
Introduction to Machine Learning
Logistic Regression
Random Forest and Decision Trees
Unsupervised learning
Denial-of-Service
Natural Language Processing
Mathematics for Data Science
Scientific Computing through Scipy
Python Integration with Spark
Deep Learning and Artificial Intelligence
Keras and TensorFlow API
Restricted Boltzmann Machine and Autoencoders
Big Data Hadoop and Spark
Tableau
MongoDB
SAS Basics
MS Excel
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
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