Skip to main content

Data Quality & its problems

Data quality is a measure of the condition of data based on the following factors

  • Accuracy
  • Uniqueness
  • Consistency
  • Reliability
  • Availability
  • Usability
  • Sufficiency

The data is generally considered high quality if it is fit for its intended uses for decision making and strategy planning.

There can be several type of quality problems in data collection.

The most common problems are

Noise and Outliers

The noise is a kind of distortion on the actual data. The outliers are considerably different than most of the other data available in the data set.

Missing Values

The missing values are incomplete data in the data set.

Duplicate

The duplicate data are repetitive data in the data set.

Popular posts from this blog

Exercise 2 - Amdahl's Law

A programmer has parallelized 99% of a program, but there is no value in increasing the problem size, i.e., the program will always be run with the same problem size regardless of the number of processors or cores used. What is the expected speedup on 20 processors? Solution As per Amdahl's law, the speedup,  N - No of processors = 20 f - % of parallel operation = 99% = 1 / (1 - 0.99) + (0.99 / 20) = 1 / 0.01 + (0.99 / 20) = 16.807 The expected speedup on 20 processors is 16.807

Exercise 1 - Amdahl's Law

A programmer is given the job to write a program on a computer with processor having speedup factor 3.8 on 4 processors. He makes it 95% parallel and goes home dreaming of a big pay raise. Using Amdahl’s law, and assuming the problem size is the same as the serial version, and ignoring communication costs, what is the speedup factor that the programmer will get? Solution Speedup formula as per Amdahl's Law, N - no of processor = 4 f - % of parallel operation = 95% Speedup = 1 / (1 - 0.95) + (0.95/4) = 1 / 0.5 + (0.95/4) Speedup = 3.478 The programmer gets  3.478 as t he speedup factor.

BITS Work Integrated Learning Program - M.Tech Data Science & Engineering

     BITS Pilani offers work integrated learning program (WILP) on M.Tech Data Science and Engineering which is UGC approved. The course is a four semester programme designed to help work professionals to build their skills required for data science engineering which enable them to become a Data Scientist.  It is a 100% online course and lectures would be delivered by BITS Pilani faculty on weekends. Those who are working in software industry as Software Engineer, Programmer, Data Analyst, Business Analyst can apply for the course. Minimum eligibility criteria to apply for the course. Those who are employed holding B.E/B.Tech/MCA/M.Sc or Equivalent with 60% marks and minimum one year relevant work experience. The candidates should have basic programming and engineering mathematics knowledge. The following subjects shall be covered in the course. Semester Subjects Data Mining Mathematical Fundamentals for Data Science Data Structure and Algorithms Design Co...