Skip to main content

BECOMING A DOCTOR TO DATA


The world of forensics is so fun and thrilling that almost every one of us at one point of time wished to be involved in the field of forensics. Seeing our favorite actors and actresses on screen do intriguing and enthralling stuff using some really mind-boggling techniques and technology always used to attract a lot of us in doing something similar right! Well, let us explore one such field of forensics and try to give you a little insight and a possible jumpstart to do something that a part of you always wished to.

IMPORTANCE OF DATA
Talking of doing riveting stuff with the best technology at your disposal, the best field to do so would certainly be in the world of data. There is no doubt and questions in regards to the fact that the field of data and its related studies have made a serious breakthrough in revolutionizing each and every industry on unprecedented levels. The power and potential of data has been identified by each and every business and organization in the world. From gaining simple insights on something to taking the biggest of decisions, the driving force behind each and every action taken by businesses is the set of data that they have: the security and maintenance of which has become a major part of concern as well as expenditure for them.

WHAT IS DATA FORENSICS?
With streams of data flowing in and around from unknown perimeters in the modern world, it has become very difficult to predict and analyze the legitimacy of its source. The advent of the internet has also made cases and events of cyber crime a common thing. Fraudulent and malicious links disguised as perfect and legitimate sources duping people of their money, information, or anything critical is something we see and hear every day. This is exactly where the field of data forensics comes into play. It involves the study and investigation of the chain of events in order to track down the lost piece of information to its source. This involves a high amount of identifying and analyzing the attributes of the digital data that was created, and then pinpointing its blueprint/mark to an exact source where whatever was lost is being kept.
The analysis of the data can either be done in a cross-drive manner: where information retrieved from various hard drives is linked together to create a web-like structure that ultimately points to its center, or using live analysis: where evidence is gathered in real time by analyzing the series tasks that the operating system of the host computer performed in order to identify something fishy and peculiar and which could be a possible source of the cybercrime.


RESOURCE BOX
The field of data science and its applications pack a huge potential in regards to a career in the modern world. Being involved in it is something that will really pay off in the times to come. Get a certification in data science in bangalore today from ExcelR one of the best institutes for data science there is.


Source : https://excelrdatascience.blogspot.com/2019/04/learn-data-science-from-experts.html

Comments

  1. Hey, thanks for this great article I really like this post and I love your blog and also Check marking analytics manager in hyderabad at 360DIGITMG.
    360Digitmg marketing analytics manager in hyderabad

    ReplyDelete
  2. This is exactly the information I'm looking for, I couldn't have asked for a simpler read with great tips like this... Thanks! ExcelR Data Scientist Course In Pune With Placement

    ReplyDelete

Post a Comment

Popular posts from this blog

Understanding Logistic Regression using R

  1. What is Logistic Regression? Logistic Regression is one of the machine learning algorithms used for solving classification problems. It is used to estimate probability whether an instance belongs to a class or not. If the estimated probability is greater than threshold, then the model predicts that the instance belongs to that class, or else it predicts that it does not belong to the class as shown in fig 1. This makes it a binary classifier. Logistic regression is used where the value of the dependent variable is 0/1, true/false or yes/no. Example 1 Suppose we are interested to know whether a candidate will pass the entrance exam. The result of the candidate depends upon his attendance in the class, teacher-student ratio, knowledge of the teacher and interest of the student in the subject are all independent variables and result is dependent variable. The value of the result will be yes or no. So, it is a binary classification problem. Practical Implementation of Logistic Regres

Data science: A Blend of These Data Components

Data Science Course ExcelR offers Data Science course, the most comprehensive Data Science course in the market, covering the complete Data Science lifecycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from  Statistical Analysis , Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics,  Machine Learning ,   Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, XLMiner,  Tableau , Spark, Hadoop, Minitab, programming languages like R programming ,  Python are covered extensively as part of this Data Science training. ExcelR is considered as the best Data Science training institute which offers services from training to placement as part of the Data Science training program with over 400+ participant

Knowledge Science, Enterprise Analytics Programs

Increase your analytics career with highly effective new Microsoft® Excel abilities by taking this Business Analytics with Excel course, which includes Energy BI coaching. Information Science is a area which is consistently evolving and constantly making manner for brand new technologies to be picked up. Many firms and professionals are mastered to stay ahead within the competition. Course Content (Score 4.5): The core course contains Statistics and likelihood, R and Python for Information Science, Hadoop, big information with Apache Spark, machine studying for information science, IBM Watson analytics, enterprise use instances of big information and data science and much more. College students had been enlightened by his profound information and business experience on Digital Advertising. This Big Information provides problem in term of storage and further analysis in rest of in real time. Information science is a combination of statistics, mathematics, computers, algorithms and a