Sevilla boss Pablo Machin expressed displeasure over the way his team dropped two’ after Saturday’s 1-1 draw at Mestalla.Mouctar Diakhaby’s last-gasp equaliser held Sevilla to their second successive draw, keeping them second in La Liga, and Machin complained about his team’s inconsistent display.“If I didn’t watch the game, I’d have said that a point was always good away from home against a super team,” the Coach said after the match as cited by Football Espana.“However, given how we played in the first half, scoring in the second and them equalising with the last kick of the game, we feel Valencia gained a point and we dropped two.La Liga Betting: Match-day 4 Stuart Heath – September 14, 2019 Despite it being very early into La Liga season, both Barcelona and Real Madrid have had unprecedented starts to their campaigns. With this in…“It’s one of those stadiums where Sevilla haven’t been able to win. We were so close to doing so that we feel a point is less than we deserve.“We fell short and it’s a shame that we didn’t get the victory that would’ve sent us 13 points clear of our opponents.“I hope we don’t regret dropping them going forward. You have to defend tooth and nail in the last few minutes, but we’re all human.”
Kolkata: West Bengal minister and TMC candidate Firhad Hakim on Monday secured 121 votes in the 144-member Kolkata Municipal Corporation (KMC) to win the mayoral election. The result of the mayoral election, conducted through a secret-ballot system, was announced by the civic body’s municipal secretary, Harihar Prasad Mondal. BJP candidate Meena Devi Purohit, who contested the polls against Hakim, bagged five votes. Twelve Left Front and two Congress councillors boycotted the election. Also Read – Rain batters Kolkata, cripples normal life Last week, CPI(M) councillor Bilquis Begum had moved the Calcutta High Court, challenging Hakim’s election on the ground that he was not a councillor from any ward of the civic body. The high court on Friday refused to stay the election. The ruling TMC has 122 seats in the KMC House, the Left Front 14, the BJP five, the Congress two, while one seat remains vacant. Sources said one TMC councillor gave Monday’s election a miss due to ill health. Also Read – Speeding Jaguar crashes into Mercedes car in Kolkata, 2 pedestrians killed Hakim, who is also the urban development and municipal affairs minister, was chosen as the mayor designate by Trinamool Congress (TMC) councillors, following Sovan Chatterjee’s resignation from the post. Chatterjee visited the KMC headquarters in New Market area of the city to cast his vote. Talking to media after the announcement of the results, Chatterjee extended his best wishes to Hakim. “I am sure the KMC will function well under the leadership of the new mayor.
Around 80% of the time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis, and reporting. Although, being an important task given its nature, there are certain myths associated with data wrangling which developers should be cautious of. In this post, we will discuss four such misconceptions. Myth #1: Data wrangling is all about writing SQL query There was a time when data processing needed data to be presented in a relational manner so that SQL queries could be written. Today, there are many other types of data sources in addition to the classic static SQL databases, which can be analyzed. Often, an engineer has to pull data from diverse sources such as web portals, Twitter feeds, sensor fusion streams, police or hospital records. Static SQL query can help only so much in those diverse domains. A programmatic approach, which is flexible enough to interface with myriad sources and is able to parse the raw data through clever algorithmic techniques and use of fundamental data structures (trees, graphs, hash tables, heaps), will be the winner. Myth #2: Knowledge of statistics is not required for data wrangling Quick statistical tests and visualizations are always invaluable to check the ‘quality’ of the data you sourced. These tests can help detect outliers and wrong data entry, without running complex scripts. For effective data wrangling, you don’t need to have knowledge of advanced statistics. However, you must understand basic descriptive statistics and know how to execute them using built-in Python libraries. Myth #3: You have to be a machine learning expert to do great data wrangling Deep knowledge of machine learning is certainly not a pre-requisite for data wrangling. It is true that the end goal of data wrangling is often to prepare the data so that it can be used in a machine learning task downstream. As a data wrangler, you do not have to know all the nitty-gritties of your project’s machine learning pipeline. However, it is always a good idea to talk to the machine learning expert who will use your data and understand the data structure interface and format he/she needs to run the model fast and accurately. Myth #4: Deep knowledge of programming is not required for data wrangling As explained above, the diversity and complexity of data sources require that you are comfortable with deep notions of fundamental data structures and how a programming language paradigm handles them. Increasing deep knowledge of the programming framework ( Python for example) will surely help you to come up with innovative methods for dealing with data source interfacing and data cleaning issues. The speed and efficiency of your data processing pipeline can often be benefited from using advanced knowledge of basic algorithms e.g. search, sort, graph traversal, hash table building, etc. Although built-in methods in standard libraries are optimized, having this knowledge gives you an edge for any situation. You read a guest post from Tirthajyoti Sarkar and Shubhadeep Roychowdhury, the authors of Data Wrangling with Python. We hope that these misconceptions would help you realize that data wrangling is not as difficult as it seems. Have fun wrangling data! About the authors Dr. Tirthajyoti Sarkar works as a Sr. Principal Engineer in the semiconductor technology domain where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. Shubhadeep Roychowdhury works as a Sr. Software Engineer at a Paris based Cyber Security startup. He holds a Master Degree in Computer Science from West Bengal University Of Technology and certifications in Machine Learning from Stanford. Don’t forget to check out Data Wrangling with Python to learn the essential basics of data wrangling using Python. Read Next 30 common data science terms explained Python, Tensorflow, Excel and more – Data professionals reveal their top tools How to create a strong data science project portfolio that lands you a job