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Certificate Associate in Data Science - Data Cleaning

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Certificate Associate in Data Science – Data Cleaning

CADS Data Cleaning

Real-world data is frequently dirty and unstructured, and must be reworked before it is usable. Data may contain errors, have duplicate entries, exist in the wrong format, or be inconsistent. The process of addressing these types of issues is called data cleaning. Data cleaning is also referred to as data wrangling, massaging, reshaping , or munging. Data merging, where data from multiple sources is combined, is often considered to be a data cleaning activity.

We need to clean data because any analysis based on inaccurate data can produce misleading results. We want to ensure that the data we work with is quality data. Data quality involves:

  • Validity: Ensuring that the data possesses the correct form or structure
  • Accuracy: The values within the data are truly representative of the dataset
  • Completeness: There are no missing elements
  • Consistency: Changes to data are in sync
  • Uniformity: The same units of measurement are used

Learning Objectives

After completing this course, you should have the skills and be familiar with the following topic

  • Handling various kind of data importing scenarios that is importing various kind of datasets (.csv, .txt), different kind of delimiters (comma, tab, pipe), and different methods (read_csv, read_table)
  • Getting basic information, such as dimensions, column names, and statistics summary
  • Getting basic data cleaning done that is removing NAs and blank spaces, imputing values to missing data points, changing a variable type, and so on
  • Creating dummy variables in various scenarios to aid modelling
  • Generating plots like scatter plots, bar charts, histograms, box plots, and so on

Who should attend

Data Analysts, Data Engineers, Data Science Enthusiasts, Business Analysts, Project Managers

Prerequisite

Foundational certificate in Big Data/Data Science

This course is meant for anyone who are comfortable developing applications in Python, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Python programming language will also find this course to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing python stack, this course is for you

Delivery Method

Mix of Instructor-led, case study driven and hands-on for select phases

H/w, S/w Reqd

Python, Pandas, Numpy, System with at least 2GB RAM and a Windows /Ubuntu/Mac OS X operating system

Duration

24 Hours (2 days Instructor led + 8 hours online learning)

Enroll Now
    • Course Name: Certificate Associate in Data Science – Data Cleaning
    • Location: Singapore
    • Duration: 2 days classroom + 8 hours online
    • Exam Time: 60 minutes
    • Course Price: Call for price
    • Minimum requirements: Foundational Certificate in Programming

ITPACS LogoITPACS Data Science Certification Road Map

Course contents

# Topic Method of Delivery
Day 1
1

Chapter 1 – Introduction to Data Cleaning

1.1 Scope of predictive modelling……………………………………………………..

1.2 Ensemble of statistical algorithms………………………………………………..

1.3 Statistical tools…………………………………………………………………………..

1.4 Data Cleaning Effort……………………………………………………………………

1.5 Tools………………………………………………………………………………………..

1.5.1 Anaconda……………………………………………………………………..

1.5.2 Pandas………………………………………………………………………….

1.5.3 NumPy………………………………………………………………………….

1.5.4 matplotlib…………………………………………………………………….

1.5.5 Jupyter Notebook………………………………………………………….

1.5.6 Scikit-learn……………………………………………………………………

Instructor Led
2

Chapter 2 – Data Cleaning Basics

2.1 DataFrames………………………………………………………………………………

2.2 Delimiters…………………………………………………………………………………

2.3 CSV………………………………………………………………………………………….

2.3.1 Reading a dataset using the read_csv method…………………..

2.3.2 Reading data from a URL………………………………………………..

2.3.3 Reading from an .xls or .xlsx file……………………………………….

2.3.4 Writing to a CSV or Excel file…………………………………………..

Instructor Led
3

 

Chapter 3 – Summaries, Structure

Instructor Led
 

Case study

Hands-on session
4

Chapter 4 – Handling missing values

4.1 Checking for missing values………………………………………………………..

4.2 Nan…………………………………………………………………………………………

4.3 Dealing with missing values………………………………………………………..

4.3.1 Deletion……………………………………………………………………….

4.3.2 Imputation……………………………………………………………………

4.4 Creating dummy variables……………………………………………………………

Instructor Led
 

Case Study

Hands-on session
Day 2
5

Chapter 5 – Data Wrangling

5.1 Selecting columns……………………………………………………………………

5.2 Selecting rows…………………………………………………………………………

5.3 Selecting a combination of rows and columns……………………………..

5.4 Creating new columns………………………………………………………………

5.5 Generating random numbers…………………………………………………….

5.6 Seeding a random number………………………………………………………..

5.7 Choice Function……………………………………………………………………….

Instructor Led
6

Chapter 6 – Aggregation, Grouping and Transformations

6.1 Grouping………………………………………………………………………………..

6.2 Aggregation…………………………………………………………………………….

6.3 Transformation………………………………………………………………………..

Instructor Led
7

Chapter 7 – Merging/Joining

7.1 Merging or joining……………………………………………………………………

7.2 Inner Join………………………………………………………………………………..

7.3 Left Join………………………………………………………………………………….

7.4 Right Join………………………………………………………………………………..

Instructor Led
8

Case Study

Hands–on session
9

Case Project

Hands–on session
10

Assignment

Online Self paced

Certification

  • Certificate Title: Certificate Associate in Data Science – Data Cleaning
  • Certificate Awarding Body: ITPACS

About ITPACS

Information Technology Professional Accreditations and Certifications Society (ITPACS) is a non-profit organization focused on improving technology skills for the future. ITPACS offers associate level, professional level and leader certifications across 6 domains including data science, web development, mobile development, cyber security, IoT and blockchain. Applicants have to go through a exam eligibility process demonstrating their experience.

Certification Roadmap

CADS Data Cleaning Outline

Eligibility

The Associate certification is catered to individuals with less than 1 year working experience in the field. This is ideal for newcomers starting out in the profession or those seeking to make an entry into the profession. Applicants are required to have completed the application process prior to taking the exam.  

Styling Eligibility

Exam

  • Exam Format: Closed-book format.
    Questions: 30 multiple choice questions, coding exercises
    Passing Score: 65%
    Exam Duration: 60 minutes
    Proctored
  • Exam needs to be taken within 12 months from the exam voucher issue date

ITPACS Certification Training Road Map

Data Science

Data science is not a single science as much as it is a collection of various scientific disciplines integrated for the purpose of analyzing data. These disciplines include various statistical and mathematical techniques, including:

  • Computer science
  • Data engineering
  • Visualization
  • Domain-specific knowledge and approaches

With the advent of cheaper storage technology, more and more data has been collected and stored permitting previously unfeasible processing and analysis of data. With this analysis came the need for various techniques to make sense of the data. These large sets of data, when used to analyze data and identify trends and patterns, become known as big data.

The process of analyzing big data is not simple and evolves to the specialization of developers who were known as data scientists. Drawing upon a myriad of technologies and expertise, they are able to analyze data to solve problems that previously were either not envisioned or were too difficult to solve.

The various data science techniques that we will illustrate have been used to solve a variety of problems. Many of these techniques are motivated to achieve some economic gain, but they have also been used to solve many pressing social and environmental problems. Problem domains where these techniques have been used include finance, optimizing business processes, understanding customer needs, performing DNA analysis, foiling terrorist plots, and finding relationships between transactions to detect fraud, among many other data-intensive problems.

Data mining is a popular application area for data science. In this activity, large quantities of data are processed and analyzed to glean information about the dataset, to provide meaningful insights, and to develop meaningful conclusions and predictions. It has been used to analyze customer behavior, detecting relationships between what may appear to be unrelated events, and to make predictions about future behavior.

Machine learning is an important aspect of data science. This technique allows the computer to solve various problems without needing to be explicitly programmed. It has been used in self-driving cars, speech recognition, and in web searches. In data mining, the data is extracted and processed. With machine learning, computers use the data to take some sort of action.