Data Science Basics

Data science is an essential part of many industries today, due to the huge amount of data produced, and is one of the most discussed topics in IT circles. Its popularity has grown over the years and companies have started implementing data science techniques to expand their business and increase customer satisfaction. In this article, we will learn what data science is and how you can become a data scientist. Burraq IT solutions provides Complete IT Training courses. Data science is a field of study that deals with large volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information and make business decisions. Data science uses complex machine learning algorithms to create predictive models. The data used for analysis can come from many different sources and can be presented in different formats.

Now that you know what data science is, next, let’s focus on the data science life cycle. The data science life cycle consists of five distinct phases, each with its own tasks:

  1. Capture
  2. Maintenance
  3. Process.
  4. Analytics
  5. Communicate

Data Science Course Details

Data science is one of the fastest growing fields in the technology sector. Simply put, there is a high demand for data specialists but a significant supply shortage. The course will prepare you for a career as a data scientist or data analyst. This program will provide you with the information you need to begin your career in data science.

Learning Objectives:

The program will provide education and hands-on training to participants so that they can begin working in the sector with confidence. Participants will learn how to manage Data Science projects throughout their life cycle by the end of this session. The course will cover the following topics:

Data Science with Python

Python is a general-purpose programming language that is gaining popularity in data science. Businesses all over the world are embracing Python to extract insights from their data and achieve a competitive advantage. Students will learn how to manage and analyze data in Python in this session. Students will learn about Python functions and loops. They will also gain hands-on experience with Jupyter Hub and Python libraries such as Pandas, NumPy, Scipy, and others.

Data Exploration and Model Development

How do we move from data to conclusions? The process of studying datasets, answering questions, and visualizing outcomes is known as exploratory data analysis. This course will teach you how to clean and validate data, as well as how to display distributions and relationships between variables. The course will cover the fundamental exploratory strategies for data summarization.

Data Development and Machine Learning

Prediction and machine learning are two of the most significant activities performed by data professionals. This course will cover the fundamentals of developing and utilizing prediction functions, with a focus on practical applications. Students will learn how to design, test, train, and deploy state-of-the-art AI and machine learning models. Students will learn supervised learning and theoretical aspects of machine learning, among other techniques. 

  • Module 1: Python
  • Module 2: R programming language
  • Module 3: Statistics
  • Module 4: Inferential statistics
  • Module 5: Regression and Anova
  • Module 6: Exploratory data analysis
  • Module 7: Supervised machine learning
  • Module 8: Tableau
  • Module 9: Machine learning on cloud

Python is the most important and necessary topic that every data scientist should have knowledge about.

  • Environment set-up
  • Jupyter overview
  • Python Numpy
  • Python Pandas
  • Python Matplotlib

Used for statistical and data analysis, R programming language is one of the advanced statistical languages used in data science. This module teaches you how to explore data sets using R. Here you will learn

  • An introduction to R
  • Data structures in R
  • Data visualization with R
  • Data analysis with R

When working with data, the knowledge of statistics is necessary and an important skill set that you must have. In this module, you will learn

  • Important statistical concepts used in data science
  • Difference between population and sample
  • Types of variables
  • Measures of central tendency
  • Measures of variability
  • Coefficient of variance
  • Skewness and Kurtosis

Inferential statistics is used to make generalizations of populations, from which samples are drawn. This is a new branch of statistics, which helps you learn to analyze representative samples of large data sets. In this module, you will learn

  • Normal distribution
  • Test hypotheses
  • Central limit theorem
  • Confidence interval
  • T-test
  • Type I and II errors
  • Student’s T distribution

This lesson will help you understand how to establish a relationship between two or more objects. ANOVA or analysis of variance is used to analyze the differences among sample sets. Here you will learn

  • Regression
  • R square
  • Correlation and causation

In this lesson you will learn

  • Data visualization
  • Missing value analysis
  • The correction matrix
  • Outlier detection analysis

This is a comprehensive module to help you understand how to make machines or computers interpret human language. You will learn –

  • Python tool
  • Support vector machine
  • Logistic and linear regression
  • Decision tree classifier
  • Neural networks

Tableau is a sophisticated business intelligence tool used for data visualization. In this lesson, you will learn

  • Working with Tableau
  • Deep diving with data and connection
  • Creating charts
  • Mapping data in Tableau
  • Dashboards and stories

In this lesson, you will learn

  • ML on cloud platform
  • ML on AWS
  • ML on Microsoft Azure

Data Science

Fee: 15,000
Duration: 1 Month
Timing: 9AM-11AM, 11AM-1PM, 1PM-3PM, 3PM-5PM, 5PM-7PM, 7PM-9PM

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