Data Science

Overview

World-renowned companies like Netflix, Google, and many IT giants have increased their business development and growth with Data Science and Machine Learning (ML) applications, directed by a data-driven tactic to decision-making. According to Analytics Insight (2020), around 77 percent of devices that we currently use are utilizing ML. To ensure business’s future readiness in today's digital era, organizations need skilled talent to leverage data science and ML's exponential budding for automation, effective decision-making, and competitive advantage. To meet this demand, the International College for Security Studies has designed a Certificate Programme in Data Science & Machine Learning. This high-impact program will help you draw on the expertise of ICSS’s prominent faculty in an immersive industry-oriented learning pedagogy to set up robust predictive as well as prescriptive models with hands-on experience in Machine Learning algorithms and statistical models. Become industry-ready with an in-depth understanding of in-demand data science and machine learning tools and techniques with Python.

Data Science Course Modules

  • 1.1 Decision trees & control
  • 1.2 Binary number system
  • 1.3 Strings
  • 1.4 Arithmetic operators
  • 1.5 Loops
  • 2.1 Time Complexity
  • 2.2 Arrays & Strings
  • 2.3 Binary Search & 2 Pointers
  • 2.4 Recursion, Hashing & Sorting
  • 2.5 Bit manipulation
  • 2.6 Stacks, Queues & Linked Lists
  • 2.7 Trees, Tries, Heap & Greedy
  • 2.8 DP, Graphs
  • 2.9 DB, OS & Computer Networks
  • 3.1 Functions
  • 3.2 Recursions
  • 3.3 Pointers
  • 3.4 Structures
  • 3.5 Unions
  • 3.6 Dynamic Arrays
  • 3.7 Asymptotic notations
  • 4.1 Sample or population data
  • 4.2 Fundamentals of descriptive statistics
  • 4.3 Measures of central tendency, asymmetry, and variability
  • 4.4 Descriptive statistics
  • 4.5 Distributions
  • 4.6 Estimators and estimates
  • 4.7 Confidence intervals
  • 4.8 Inferential statistic
  • 4.9 Hypothesis Testing
  • 4.10 Regression analysis
  • 4.11 Subtleties of regression analysis
  • 4.12 Assumptions for linear regression analysis
  • 5.1 Fundamentals of Python
  • 5.2 Python Environment Setup and Essentials
  • 5.3 Mathematical Computing with Python (NumPy)
  • 5.4 Scientific computing with Python (Scipy)
  • 5.5 Data Manipulation with Pandas
  • 5.6 ML with Scikit–Learn
  • 5.7 NLP with Scikit Learn
  • 5.8 Data Visualization in Python using matplotlib
  • 5.9 Web Scraping with Python Using Beautiful Soup
  • 5.10 Python integration with Hadoop MapReduce and Spark
  • 6.1 Data wrangling
  • 6.2 Data manipulation
  • 6.3 Supervised learning
  • 6.4 Feature engineering
  • 6.5 Supervised learning-classification
  • 6.6 Time series modeling
  • 6.7 Recommender systems and text mining
  • 7.1 AI and Deep Learning
  • 7.2 Deep Learning with Tensor Flow and Keras
  • 7.3 Convolutional Neural Networks (CNN)
  • 7.4 Recurrent Neural Networks (RNN) and Autoencoders
  • 7.5 Unsupervised Learning
  • 8.1 Overview of Tableau
  • 8.2 Getting Started
  • 8.3 Connect to Data
  • 8.4 Data Visualisations
  • 8.5 Other Functionalities
  • 8.6 Filters
  • 8.7 Drill Down and Up
  • 8.8 Forecasting
  • 8.9 Trend Lines
  • 8.10 Clustering
  • 8.11 Dashboard

Capstone Project: AI and Machine Learning

In this capstone, learners will apply their deep learning knowledge and expertise to a real-world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model, and validate it.

BOOK YOUR DEMO NOW

What are you waiting for?

Pick up your phone & register your seat for Cyber Security & Ethical Hacking Training & Certification courses. If you still have any kind of doubts feel free to contact our counselor.

Program Details

By the end of this course, students will:

  • Gain a comprehensive understanding of data structure and manipulation
  • Comprehend and use linear and non-linear regression models and techniques of classification for data analysis
  • Get a thorough understanding of supervised as well as unsupervised learning models
  • Carry out technical and scientific computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
  • Comprehend the different mechanisms of the Hadoop ecosystem
  • Gain expertise in mathematical computing using the Scikit-Learn package and NumPy
  • Know MapReduce and its characteristics, plus inculcate how to ingest data using Sqoop and Flume Master the concepts recommendation engine, and time series modelling and gain practical mastery over principles, algorithms, and applications of Machine Learning
  • Learn to work with HBase, its architecture and data storage, learning the difference between HBase and RDBMS, and use Hive and Impala for partitioning
  • Learn to analyze data using Tableau and become proficient in building interactive dashboard

Copyright © 2021 by ICSS. All Rights Reserved.