Featured Post

Python Regex: The 5 Exclusive Examples

-
Image
 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations: 01 Matching a Simple Pattern import re text = "Hello, World!" pattern = r"Hello" result = re.search(pattern, text) if result:     print("Pattern found:", result.group()) Output: Output: Pattern found: Hello This example searches for the pattern "Hello" in the text and prints it when found. 02 Matching Multiple Patterns import re text = "The quick brown fox jumps over the lazy dog." patterns = [r"fox", r"dog"] for pattern in patterns:     if re.search(pattern, text):         print(f"Pattern '{pattern}' found.") Output: Pattern 'fox' found. Pattern 'dog' found. It searches for both "fox" and "dog" patterns in the text and prints when they are found. 03 Matching Any Digit   import re text = "The price of the
Post a Comment

4 Top Data Scientist Skills to be Successful

-
Data science is a combination of technical and general skills. As an analyst, you are responsible to provide useful information to the client. Below is a useful list of skills.

Top Data Scientist Skills.


4 Important Skills You Need for Data Scientists

1. Paradigms and practices.

This involves data scientists acquiring a grounding in core concepts of data science, analytics, and data management. 

Data scientists should easily grasp the data science life cycle, know their typical roles and responsibilities in every phase, and be able to work in teams and with business domain experts and stakeholders. 

Also, they should learn a standard approach for establishing, managing, and operationalizing data science projects in the business.

2. Algorithms and modeling.

Here are the areas with which data scientists must become familiar:
  • linear algebra, 
  • basic statistics, 
  • linear and logistic regression, 
  • data mining, 
  • predictive modeling, 
  • cluster analysis, 
  • association rules, 
  • market-basket analysis, 
  • decision trees, 
  • time-series analysis, 
  • forecasting, machine learning, 
  • Bayesian and Monte Carlo Statistics, 
  • matrix operations, 
  • sampling, 
  • text analytics, 
  • summarization, 
  • classification, 
  • primary components analysis, 
  • experimental design and unsupervised learning-constrained optimization.

3. Tools and platforms

Data scientists should master a basic group of modeling, development, and visualization tools used on your data science projects, as well as the platforms used for storage, execution, integration, and governance of big data in your organization.


Depending on your environment, and the extent to which data scientists work with both structured and unstructured data, this may involve some combination of : 

  • data warehousing, Hadoop, stream computing, NoSQL, and other platforms. 
  • It will probably also entail providing instruction in MapReduce, R, and other new open-source development languages in addition to SPSS, SAS, and any other established tools.

4. Applications and outcomes.

A major imperative for data scientists is to learn the chief business applications of data science in your organization, as well as ways to work best with subject-matter experts: 
  • In many companies, data science focuses on marketing, customer service, the next-best offer, and other customer-centric applications. 
  • Often, these applications require that data scientists know how to leverage customer data acquired from structured survey tools, sentiment analysis software, social media monitoring tools, and other sources. 
  • Plus, every data scientist must understand the key business outcomes—such as maximizing customer lifetime value—that should be the focus of their modeling initiatives.

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

Explained Ideal Structure of Python Class

-
Image
When you are designing a class, you need to ensure that the classification of its critical parts is outlined at the beginning. The clearer the initial design, the more performant and scalable the class is. Some of the components in the order in which they should be defined in the class are mentioned as follows. Ideal structure of a class Class variables Constants or default variables are usually defined at the top of the class. For someone who is reading the code, it comes as an easy-to-view consolidated list, and for the interpreter it ensures that all such variables are processed before diving into the main logic of the class, including any other Instance method or constructor. The __init__ method The __init__ method provides information about inputs needed and how to instantiate the class. It is also the constructor of the class, which the very first method called while initializing the class . Special Python methods These methods change the functionality of the class or provide add
Read more

6 Python file Methods Real Usage

-
Image
Files always have data in them. For different purposes, you need methods to deal with them. These six file methods of use in this case. Related:  5 top file modes in python. Python file methods Below are the top six file methods in python: Tell() Seek() Flush() Fileno() truncate() isatty() 1. tell() The usage of tell method is it  returns the cursor-position of the file-pointer from the beginning of the file. It tells the current cursor position. The position starts from zero, which is same as of an index. Syntax:  fileobject.tell() 2. seek() The usage of seek  method is cursor position, from one position to the specified position from the beginning of the file, it moves. Here, the cursor will be sought to a particular location. Syntax:  fileobject.seek(position) 3. flush() The usage of flush  method is clean out the internal buffer. Before writing the text in the file, it is best practice to clear out that the internal buffer can be cleared. Syntax:  fileobject.flush() 4. fileno() The
Read more

How to Decode TLV Quickly

-
Image
TLV format contains three parts Tag, Length, and value. In a credit card or financial transactions, the TLV protocol supports this format. Below, you will find the ideas to decode TLV data quickly.  According to IBM , the tag tells what type of data it is. The length field denotes the length of the value. The value-field denotes the actual value. Structure of TLV. TLV comprises three field values. Tag Length Value How to Decode TLV The EMV labs developed tags which in turn part of EMV protocol. Each tag has an unique meaning. And the Tag and Length together takes 1 to 4 bytes of memory. 1. The Best example for TLV. Below is the way to decode the EMV tag. The first part of the TLV format is TAG. The second part is LENGTH, and finally the VALUE. Syntax of EMV tag: [Tag][Value Length][Value] (ex. " 9F40 05 F000F0A001 ") where,  Tag Name =  9F40 Value Length (in bytes) =  05  Value (Hex representation of bytes. Example, "F0" – 1-byte) =  F000F0A001 Finally, what is 9F40
Read more