How To Become A Data Scientist

  8.  Counta() COUNTA determines whether a cell is empty or not. You’ll come across incomplete data sets daily as a data analyst. Without needing to restructure the data, COUNTA will allow you to examine any gaps in the dataset. SYNTAX = COUNTA (value1, [value2], …) 9. Vlookup() The acronym VLOOKUP stands for ‘Vertical Lookup.’ It’s a function that tells Excel to look for a specific value in a column (the so-called ‘table array’) to return a value from another column in the same row. SYNTAX = VLOOKUP (lookup_value, table_array, column_index_num, [range_lookup]) 10.  Hlookup() “Horizontal” is represented by the letter H in HLOOKUP. It looks for a value in the top row of a table or an array of values, then returns a value from a row you specify in the table or array in the same column. When your comparison values are in a row across the top of a data table and you wish to look down a specific number of rows, use HLOOKUP. When your comparison values are in a column to the left of the data

  5.  Sumifs() One of the “must-know” formulas for a data analyst is =SUMIFS. =SUM is a familiar formula, but what if you need to sum data based on numerous criteria? It’s SUMIFS. SYNTAX = SUMIFS (sum_range, range1, criteria1, [range2], [criteria2], …) 6.  Averageifs() AVERAGEIFS, like SUMIFS, lets you take an average based on one or more parameters. SYNTAX = AVERAGEIFS (avg_rng, range1, criteria1, [range2], [criteria2], …)

  3.  Days() The number of calendar days between two dates is calculated using this function = DAYS. SYNTAX =DAYS (end_date, start_date)   4.  Networkdays The number of weekends is automatically excluded when using the function. It’s classified as a Date/Time Function in Excel. The net workday’s function is used in finance and accounting for determining employee benefits based on days worked, the number of working days available throughout a project, or the number of business days required to resolve a customer problem, among other things. SYNTAX = NETWORKDAYS (start_date, end_date, [holidays])

  2.  Len() In data analysis, LEN is used to show the number of characters in each cell. It’s frequently utilised when working with text that has a character limit or when attempting to distinguish between product numbers. SYNTAX = LEN (text)

  Essential Excel Data Analysis Functions Excel has hundreds of functions and trying to match the proper formula with the right kind of data analysis can be overwhelming. It is not necessary for the most valuable functions to be difficult. You’ll wonder how you ever lived without fifteen easy functions that will increase your ability to interpret data. 1.  Concatenate When conducting data analysis, the formula =CONCATENATE is one of the simplest to understand but most powerful. Text, numbers, dates, and other data from numerous cells can be combined into a single cell. SYNTAX = CONCATENATE (text1, text2, [text3], …)

  Data analysis is a valuable skill that can help you make better judgments. Microsoft Excel is one of the most used data analysis programs, with the built-in pivot tables being the most popular analytic tool. Microsoft Excel allows you to examine and interpret data in a variety of ways. The information could come from several different places. A variety of formats and conversions are available for the data. Conditional Formatting, Ranges, Tables, Text functions, Date functions, Time functions, financial functions, Subtotals, Quick Analysis, Formula Auditing, Inquire Tool, What-if Analysis, Solvers, Data Model, PowerPivot, PowerView, PowerMap, and other Excel commands, functions, and tools can all be used to analyse it.

  The iterative Data Analysis Process is comprised of the following phases: • Specification of Data Requirements • Data Gathering • Data Processing • Data Cleaning • Data Analysis • Data Communication  

  Table of Contents Introduction 15 Essential Excel Data Analysis Functions Methods for Data Analysis in Excel Data Analysis with Microsoft Excel Simple Linear Regression Model in Microsoft Excel Dataset   Introduction to Excel for Data Analysis Data analysis is the process of cleansing, transforming, and analyzing raw data to obtain usable, relevant information that can assist businesses in making educated decisions. By giving relevant insights and data, which are commonly presented in charts, photos, tables, and graphs, the technique helps to lessen the risks associated with decision-making. Data analytics encompasses not just data analysis, but also data collecting, organization, storage, and the tools and techniques used to delve deeper into data, as well as those used to present the findings, such as data visualization tools. On the other hand, data analysis is concerned with the process of transforming raw data into meaningful statistics, information, and explanations. Data visua

  Basics of Data Structure and Algorithm: What is Data Structure:   A data structure is a storage that is used to store and organize data. It is a way of arranging data on a computer so that it can be accessed and updated efficiently. A data structure is not only used for organizing the data. It is also used for processing, retrieving, and storing data. There are different basic and advanced types of data structures that are used in almost every program or software system that has been developed. So we must have good knowledge about data structures.  Classification of Data Structure:   Linear data structure:  Data structure in which data elements are arranged sequentially or linearly, where each element is attached to its previous and next adjacent elements, is called a linear data structure.  Examples of linear data structures are array, stack, queue, linked list, etc. Static data structure:  Static data structure has a fixed memory size. It is easier to access the elements in a stati

  What is Data Science? Data science is the science of analyzing raw data using statistics and machine learning techniques with the purpose of drawing conclusions about that information. Why Data Science?   Data Science has become a revolutionary technology that everyone seems to talk about. Hailed as the ‘sexiest job of the 21st century’, Data Science is a buzzword with very few people knowing about the technology in its true sense. While many people wish to become Data Scientists, it is essential to weigh the pros and cons of data science and give out a real picture. In this article, we will discuss these points in detail and provide you with the necessary insights about Data Science. How to Learn Data Science? Usually, data scientists come from various educational and work experience backgrounds, most should be proficient in, or in an ideal case be masters in four key areas. Domain Knowledge Math Skills Computer Science Communication Skill 1. Domain Knowledge: Most people thinking t