Data Analysis Course Curriculum

Course Curriculum

•   Introduction to Data analytics

  •    What is data analytics?
  •    Why become a data analyst?
  •    Who needs or which industries needs data analysts?
  •    Why is data analytics important?
  •    Introduction to the in-demand tools that will make you function effectively as a data analyst; Microsoft Excel, SQL, Python, Power Bi

  Data analytics in Microsoft Excel

•    Data analysis steps
•         Setting up your environment
•         Navigating the worksheet environment
•    Exploring data in excel
•         Data trimming, Sorting and Filtering tables, Nesting Functions
•         Understanding and classifying the data types, changing to text, rounding values
•    Preparing the data in exce
•         Cleaning the data, joining text strings, capitalizing words, using upper and lower case, extracting text from cells, counting characters, extracting text, replacing characters
•         Preparing date data, finding days of the week, finding the matching column
•         Using Vlookup, Hlookup, index, pivot tables and case studies
•    Analyzing the data
•         Using count, counta, countblank, count if functions
•         Math functions in Excel
•         Using Minimum, Maximum, Averages to aggregate data
•         Logical funtions
•         Conditional aggregation using the if functions
•    Visualizing the data with charts
•         Getting familiar with the different visuals available on Excel and when to use them
•         Analyzing data with line and area charts
•         Analyzing data with stacked area chart and 100 percent stacked chart
•         Building a dashboard in Excel




  Data analytics in SQL (MYSQL)

•    What is SQL?
•    What is Databases, advantages of databases, Databases tables
•    How to create database, constraints types, tables, drop tables, alter tables, primary key, foreign key
•    Defining data types, unique id, defining your data types, inserting values, null values
•    Introduction to queries
•         The use of select, select distinct, select top, limit, update, delete, order by, where, having, and or not
•         Aggregation function; Min, Max, Count, Avg, Sum
•         The use of wildcards, in, as and like functions, like, as and Aliases functions, between
•    Introduction to joins
•         inner joins, outer join, cross join, right join, left join, full join
•         Set theory for SQL Joins, union and union all, intersect and except, group by
•    SQL Operators
•         SQL arithmetic operator
•         SQL Bitwise operators
•         Comparison operators
•         SQL Views/ Null functions
•         SQL Comments
•         Case




  Data analytics in Python

•    What is python?
•    Python syntax, comments, variables
•    Python data types
•         Numeric data, strings, sequence types
•         Mapping data type
•         Boolean types
•         Numbers, casting
•    Conditional statements
•         If and else statements
•    Anaconda - Jupyter notebook
•         Python modules
•         Numpy arrays, creating arrays, arrays indexing, array slicing
•         Pandas; Pandas series, Dataframe, Read CSV, cleaning data, dealing with empty data, removing duplicates, pandas plotting
•         Matplotlib; intro to matplotlib, the use of pyplot, plotting, markers, line, labels, subplot, scatter, bars, histogram and pie charts
•         Seaborn; using pandas with seaborn




  Data analytics in Power bi

•    Navigating the powerbi environment
•         Introduction to powerbi
•         Features of powerbi
•         Loading existing reports
•         Adding cards
•         Slicers and tables
•         Slicers
•    Transforming data
•         Cleaning data
•         Power query editor; renaming and re ordering of columns, finding anomalies
•         Field aggregation, data manipulations
•         Transforming and formatting columns
•         Formatting currency
•         Making maps with geographic data
•    Visualization data
•         Visualization options; dashboards or reports, tables and scatter charts, bubble charts, KPIs, guage
•         Conditional formatting
•         Sorting data duplicates, pandas plotting
•         Dax in power bi, context Dax formulas, date data bars, histogram and pie charts
•    Data modeling in power bi
•         Load and transforming data
•         Dimensional modeling
•         Facts and dimensional table modeling
•         Breaking tables into multiple tables
•         Finding relationships between tables
•    Case study analyzing customers churn