Einar  Hintz

Einar Hintz


A Brief History of Data Science in Legal Tech

Picture a lawyer! One of the first images that will spring to mind for many is that of people in suits, labouring over tremendous piles of printed case law. Although suits are still the norm, the rest of this picture has become somewhat redundant. For the most part, the legal industry is characterized as being quite conservative and lethargic regarding innovation. Still, even the digital revolution as severely altered the landscape.

Software and digital services which either support law professionals in their day-to-day workings or automate processes entirely are described as Legal Tech. Academic literature on this field is quite sparse since for the longest time the technological solutions law professionals utilized were quite conservative, holding with the aforementioned slow-to-adapt stereotype. This is sure to change as innovation in legal tech is quickly gaining momentum — but more on this later — lets first look at where it all started.

The first wave — sometimes dubbed LegalTech 1.0 — swept over the legal realm since the early 1980s, slow at first but becoming ever more prevalent as technology progressed. The most prominent facet of this wave was digitalisation, with Legal Databases such as Westlaw popping up. These databases, at first available through massive terminals installed in law offices, replaced the tremendous piles of printed case law, with online portals such as EUR-Lex making EU law, case-law and much more available online at the press of a button in current times. Although revolutionary to how lawyers could access the data necessary for their job, this did not yet introduce data science to the legal profession.

Mark Cohen of Forbes went as far as describing the legal sector as a “data wasteland in the digital era”. Next to its generally conservative characterization, there is one straightforward reason for this; to date, the incentive for innovation was laking. Law is very labour intensive, but the (often enormous) labour costs that are raked up can be passed on to the client — there is no need to reduce them through technology. But this is beginning to change — LegalTech 2.0 is gaining momentum — with data science at the forefront of revolutionizing Law and the legal landscape.

LegalTech 2.0 ventured further than solely aiding the day to day workings at law practices — instead automating varying processes at law firms. This happens across the board; both for internal operations such as intelligent legal billing (e.g. Apperio) or knowledge-management (e.g. Intelllex); as well as for client-facing processes. One striking example of this is Flightright — which enables consumers to claim compensation for cancelled flights entirely online.

But what is it that has been able to break through the technological lethargy of Law? There are two factors at play here. For one, law firms are beginning to realize that data is essential to streamlining internal operations — enabling them to reduce costs that they are unable to pass onto their clients, assess and mitigate risks and measure, interpret and utilize performance figures. But perhaps, more importantly, external pressures are forcing them to accept and strive for innovation. Clients pressure is maybe the most dramatic factor. But many others, ranging from a changing demographics to competitive pressures introduced by legislation — such as the Legal services act of 2007 in the UK (enabling alternative business structures (ABS) in the law industry), also contribute.

With LegalTech our gaze should be set ahead with great anticipation, both for a broadening of 2.0 solutions and for LegalTech 3.0 which promises to fully automate legal processes. Disrupting the legal landscape dramatically! Putting aside the internal processes which invite Data Science solution, instead of focusing on Client-facing services, Data Science can find a wide range of implementation.

Machine learning alone can enable much more than its apparent utilization regarding the evaluation of law databases and discovery — which used to be incredibly labour intensive. Case assessment and prediction, which used to be a skill only acquired over time based on experience, can be made available across the board. By feeding case data into machine learning algorithms, that can then help predict the outcomes of potential or current cases. predictive analysis can be valuable to law professionals across the board. Data Science can be used to get insight on the opposition more effectively, to fine-tune cases for specific judges based on their past rulings and much more.

#datascienceinlegaltech #technology #data-science #legaltech #law

What is GEEK

Buddha Community

A Brief History of Data Science in Legal Tech
Uriah  Dietrich

Uriah Dietrich


How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

'Commoditization Is The Biggest Problem In Data Science Education'

The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.

IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.

With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.

Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.

#people #data science aspirants #data science course director interview #data science courses #data science education #data science education market #data science interview

Ananya Gupta

Ananya Gupta


What Are The Advantages and Disadvantages of Data Science?

Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.

Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.

Some Of The Advantages Are Mentioned Below:-

Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.

Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.

Also Read: How Data Science Programs Become The Reason Of Your Success

Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-

Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.

Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.

#data science training in noida #data science training in delhi #data science online training #data science online course #data science course #data science training

Java Questions

Java Questions


50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india