Python for Data Analysis Training - INTRODUCTION TO DATA ANALYTICS

@poshemtechnologiesinstitute (Cohort 2) Data analysis in a broad sense can be defined as the process of collection, manipulation (inspecting, cleaning, transforming), interpretation and presentation (modelling data with the goal of discovering useful information from which relevant conclusions can be drawn and informed decisions can be taken. It requires the deployment of a variety of techniques/skills and/or methods to effectively collect relevant data sets for analysis and generate relevant output for decision making. Some Techniques skills and/or methods commonly deployed in data analysis include • Statistical Analysis • Machine Learning (ML) • Data Visualization • Data Mining and Scraping, etc The relevance of Data Analytics need not be over-emphasized to buttress its importance in Finance Industry, Healthcare, Agriculture and all aspects of Human endeavour where data can be generated. Essentially, it involves collecting data from past occurrence to interpret current occurrences or forecast future. Hence, depending on whether the purpose for Data Analysis is Quantitative, tools such as Regression and/or Hypothesis Analysis are employed; or Quantitative, tools such as Content and/or Discourse Analysis are employed. Having gained invaluable insights from analysed data, market trends, customer behaviour, operational efficiency amongst others can be improved to increase profitability of a business. In consideration of Tools useful in Carrying out Data Analysis: Excel, SQL, Python, R, Tableau, Power BI etc have proven worthy amongst others. With Python, the process of data analysis would typically flow as Data (Collection Cleaning Analysis Visualization) Data Collection involves retrieving data from various sources in a pre-determined or methodical manner for processing to obtain relevant insights called information. The collection of data may be quantitative or qualitative. Sources of such data includes Databases, APIs, web-scraping etc. It involves deploying processes such as Surveys/Quizzes and/or Questionnaires, Interviews, Focus Groups, Direct Observations, Document and Records, Web scraping/harvesting etc. Data Cleaning involves the preparation of data for analysis by ordering in a sensible manner, removal of missing values and duplicates, outliers etc. Data Analysis contextually involves the actual process of deploying analytic tools for manipulation of data to derive meaningful insights for informed decision making. Data Visualization is the interpretation of already analysed data in a systematic manner using graphs, charts, animations, pictograms or any other relevant visual display that demystifies the complexity of the Output or result for effective information extraction. ADVANTAGES AND DISADVANTAGES OF USING PYTHON AS A TOOL FOR DATA ANALYSIS As a tool for carrying out Data Analysis, Python is easy to learn because of its easy syntax compared to other programming languages, its flexibility, scalability and versatility, availability of powerful Libraries (Pandas, Numpy, Matplotlib, SciPy and Seaborn) which help to save time and effort by providing pre-written codes for achieving specific tasks; and its open-source characteristics making it free to use and improve. NOTE: (I.) • Pandas - needed for data manipulation and analysis by providing data structures like DataFrames and Series • Numpy - needed for numerical computing by providing arrays and matrices for mathematical operations • Matplotlib - needed for visualization of data by providing charts, graphs and plots. • SciPy - needed for Technical, Scientific and engineering computing (II.) Since data Analyst code and test, there is need for a Notebook such as can be seen in Google colab. Colab which is an online platform serving the role of an IDE (Integrated Development Environment) where codes can be easily written and executed. CODE EXTENSIONS: .py script extensions runs script codes in batch || .pynb script extensions runs script codes per line. Unlike software programmers, mobile/web developers etc, Data Analyst commonly test script codes separately and not run them in batches which is why in practice the. pynb script extension is used. There are not much disadvantages in using python except it takes time to master it due to its robustness and its slowness when dealing with real-time applications and large data scale. #dataanalysis #python #pythonprogramming #datatypes #functions #variable #howto #basicconcepts #introduction #collection #cleaning #analysis #visualization Connect with us ?- info@poshemtech.com ?- +1 (404) 566-1526 Linkedin - https://www.linkedin.com/company/poshem-technologies/ Visit ?- www.poshemtech.com

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