Data science
is the field of large volumes of data intended to provide meaningful
information based on large amounts of complex data. Data science, or data
science-oriented, the combination of different fields of work in statistics and
computation to interpret data for decision making.
Debugging the data:
Data cleansing
or data cleansing is the process of detection and correction (or corrupted
records or inaccurate set of tabs, tables or databases and refers to the
identification of a complete piece, against all logical parts with care or
irrelevant data and replacement, modification of data and / or deletion dirty
or rough.
Techniques:
As we know,
the data obtained show inconsistencies, errors, strange characters, the value
of the loss situation or problems.In other than this, it is necessary to clean
or remove Before using this data, therefore, to rub data, various techniques
that They are used As follows:
- line filter
- Extract column or a certain word
- Change the value
- With the value lost
- Convert data from other formats
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known about → difference between data science and cloud computing
Line:
The first
cleaning operation is the high line, which means that from the data entry, each
line will be counted to determine if it can be sent as an output.
• By Location
According to
your location, the simplest form of line filter. The benefits of checking for
example the 5-line file or when you want to draw a line from the output of one
command line tool to another.
• According to the standard
If you want to
extract or delete the baseline in the content, use grep, which is a command
line tool to filter the canonical line. You can print all the rows that
correspond to a specific pattern or a regular expression.
• According to the randomness
When you are
in the process of formulating a pipeline data set and have a lot of data, debugging
can be tricky for plumbing. In this case, sampling the data can be useful. The
main reason for the command line sample is to get a subset of the data, issued
only a percentage of the input, line by line.
Deleting the change in value:
TR command
line tool, which means translate, which can be used to change the character of
the individual.
Related Blogs
→ What is Data Science
Close the loss of value:
Data mining
methods vary in the way they treat the amount lost. Usually, they ignore the
value of loss or deletion of any record that contains the missing values or
to change the value lost by the mean value or the inference of failure values.
Examples of
data cleansing and method data in Excel cleaning
1. Get rid of
extra space
2. Select and
treat all empty cells
3. Figures
change store as text for numbers
4. Remove
Duplicate
5. Highlight
errors
6. Change the
text to the appropriate bottom / top / cover
7. spell check
8. Remove all
formatting
Data Cleaning tools
Here are some
interesting tools related to data cleaning, analysis and modeling,
Jasper — open
source software such as SPSS statistics with the support of cos
Rattle — GUI
for easy-to-use language learning machines
RapidMiner — another
point and click on the package learning machine
Orange — Open
source GUI for easy-to-use machines that are taught with Python
Talend data
preparation — data cleansing, preparation and intelligence
Trifacta
Wranger — data cleansing, preparation and matching characteristics, for
example,
All of them
are open source, or free versions focus on cleaning, data analysis and
modeling.
Conclusion
Data cleansing
is a natural part of the scientific data process. In simple terms, the process
can be divided into four steps: data collection, data cleansing, data analysis
/ modeling, and publication of relevant results to the public. When trying to
skip the data cleansing steps, they often have a hard time getting the raw data
to work with traditional tools to analyze, for example, R or Python.
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