Introduction to linear algebra, matrices operations, introduction to statistics, measures of central tendencies, measures of variance, measures of frequency, what are analytics and data science?, business analytics vs. Data analytics vs. Data science, common terms in analytics, analytics vs. Data warehousing, olap, mis reporting, types of data (structured vs. Unstructured vs. Semi structured), relevance of analytics in industry and need of the hour, critical success drivers, overview of analytics tools & their popularity, analytics methodology & problem-solving framework, stages of analytics.

• Introduction to data handling, introduction to excel environment, formatting and conditional formatting, data sorting, filtering and data validation, understanding of name ranges.
• Data visualisation in excel, overview of chart types – column and bar charts, line and area charts, pie charts, doughnut charts, scatter plots, how to select right chart for your data, chart formatting, creating and customizing advance charts – thermometer charts, waterfall charts, population pyramids.
• Data manipulation using functions, descriptive functions: sum, count, min, max, average, counta, countblank, logical functions: if, and, or, not, relational operators > >= < <= !=, nesting of functions, date and time functions: today, now, month, year, day, weekday, networkdays, weeknum, time, minute, hour, text functions: left, right, mid, find, length, replace, substitute, trim, rank, rank.Avg, upper, lower, proper, array functions: sumif, sumifs, countif, countifs, sumproduct, use and application of lookup functions in excel: vlookup, hlookup, limitations of lookup functions, using index, match, offset, concept of reverse vlookup.
• Data analysis and reporting, data analysis using pivot tables – use of row and column shelf, values and filters, difference between data layering and cross tabulation, summary reports, advantages and limitations, change aggregation types and summarisation, creating groups and bins in pivot data, concept of calculated fields, usage and limitations, changing report layouts – outline, compact and tabular forms, show and hide grand totals and subtotals, creating summary reports using pivot tables.
• Overview of dashboards, what is dashboard & excel dashboard, adding icons and images to dashboards, making dashboards dynamic.
• Create dashboards in excel – using pivot controls, concept of pivot cache and its use in creating interactive dashboards in excel, pivot table design elements – concept of slicers and timelines, designing sample dashboard using pivot controls, design principles for including charts in dashboards – do’s and don’t’s.

• Basics rdbms concepts, data types in sql, example of database design, types of relationship. (primary key & foreign key)
• Utilising the object explorer, what is sql – a quick introduction, installing ms sql server for windows os, introduction to sql server management studio, understanding basic database concepts, getting started.
• Data based objects creation, types of relationship (primary key & foreign key), drop & truncate statements – uses & differences, alter table & alter column statements.
• Data manipulation, merging data, union and union all – use & constraints, intersect and except statements, table joins – inner join, left join, right join, full join, sub-queries, optimizing your work.

• Introducing power bi, introduction to power bi, installing power bi desktop (signup for powerbi), various options in power bi desktop, views in power bi desktop, etl vs elt.
• Data preparation and modelling, connect and retrieve data from different sources (csv, excel, etc.), query editor in power bi, power query for cleaning the data, power query functions – text, date, numeric, power query conditional columns, clean & transform data with query editor, work with relationships and cardinality, combining data – merging & appending, types of relationships (1:1, 1, many:1)
• Data analysis expressions, introduction to dax, calculated tables, columns & measures, time intelligence in dax, frequently used dax functions in real-time (calendar functions, filter functions, information functions, text functions, logical functions, math functions, etc.), reports development, introduction to work with power bi visuals.
• Reports development in power bi, working with different visuals/charts, formatting options in reports, use a slicer to filter visualizations, working with filters (page level, include/exclude, report level, cross report filter), download & use custom visuals from the gallery, add an r or python visual, work with key performance indicators.

• Python essentials (core), overview of python – starting with python, why python for data science?
• Anaconda vs python, introduction to installation of python, introduction to python ide’s (jupyter, ipython), concept of packages – important packages, installing & loading packages & name spaces, data types & data objects/structures (strings, tuples, lists, dictionaries), list and dictionary comprehensions, variable & value labels – date & time values, basic operations – mathematical/string/date, control flow & conditional statements, debugging & code profiling, python built-in functions (text, numeric, date), user defined functions – lambda functions, concept of apply functions, python – objects – oops concepts, how to create & call class and modules?
• Overview of pandas, what is pandas, its functions & methods, pandas data structures (series & data frames), creating data structures (data import – reading into pandas)
• Operations with numpy (numerical python), what is numpy?, overview of functions & methods in numpy, data structures in numpy, creating arrays and initializing, reading arrays from files, special initializing functions, slicing and indexing, reshaping arrays, combining arrays, numpy maths.
• Data cleaning and transformation, understand the data, sub setting / filtering / slicing data, using [] Brackets, using indexing or referring with column names/rows, using functions, dropping rows & columns, mutation of table (adding/deleting columns), renaming columns or rows, sorting (by data/values, index), by one column or multiple columns, ascending or descending, type conversions, setting index, handling duplicates /missing/outliers, creating dummies from categorical data (using get_dummies()), applying functions to all the variables in a data frame (broadcasting)
• Data visualization with python, introduction to data visualization, plotting with matplotlib, plotting with seaborn, visualizing geospatial data (folium package – maps with marker), waffle charts, wordcloud, regression plots.
• Data analysis using python, exploratory data analysis, descriptive statistics, frequency tables, and summarization, uni-variate analysis (distribution of data & graphical analysis), bi-variate analysis (cross tabs, distributions & relationships, graphical analysis), linear regression using sklearn, accuracy metrics (rmse & mse), text cleaning using regex, sentiment analysis using textblob.

• Introduction to tableau: what is tableau?, tableau architecture, connecting to data & introduction to data source concepts, understanding the tableau workspace, dimensions and measures, data types & default properties, shelves & marks card, using show me, saving and sharing your work-overview.
• Data handling & summaries: date aggregations and date parts, cross tab & tabular charts, totals & subtotals, different types of charts and visualizations, creating filters and parameters, individual axes, blended axes, dual axes & combination chart, trend lines, reference lines, forecasting, creating dashboards and data stories.
• Building maps: explain latitude and longitude, default location/edit locations, building geographical maps.
  Table calculations: percent of total, running / cumulative calculations, introduction to lod (level of detail) expressions, user applications of table calculations.
• Calculated field: basic functions (string, date, numbers, etc), usage of logical conditions.

• Data importing/exporting, introduction r/r-studio – gui, concept of packages – useful packages (base & other packages), data structure & data types (vectors, matrices, factors, data frames, and lists), importing data from various sources, exporting data to various formats, viewing data (viewing partial data and full data), variable & value labels – date values.
• Data manipulation, creating new variables (calculations & binning), dummy variable creation, applying transformations, handling duplicates/missing data, sorting and filtering, sub-setting (rows/columns), appending (row/column appending), merging/joining (left, right, inner, full, outer), data type conversions, renaming & reshaping, control structures (if, if else), loops (conditional, iterative loops), aggregation/summarization.
• Data analysis using r, introduction to exploratory data analysis, descriptive statistics, frequency tables, and summarization, uni-variate analysis (distribution of data), bivariate analysis (cross tabs, distributions & relationships)
• Data visualization with r, basic visualization tools, bar charts/histograms/pie charts, scatter plots, line plots and regression plots, word clouds, box plots, visualization using ggplot.
• Statistical methods & hypothesis testing using r, descriptive vs. Inferential statistics, what is a probability distribution?, important distributions (discrete & continuous distributions), deep dive of normal distributions and properties (central limit theorem), concept of hypothesis testing, statistical methods – z/t-tests, anova, correlation and chi-square.
• Linear regression using r, introduction – applications, assumptions of linear regression, building linear regression model, understanding standard metrics, interpretation of results, download course brochure.

Overview of the alteryx course and fundamental concepts, using the select tool to rename fields, change the data type, understanding the user environment and alteryx settings, remove from data set, understanding alteryx designer, connect to data sources, alteryx designer interface, set data field types, user and workflow settings, remove and rename fields, filtering data/data processing, blending/join data from different sources, filtering using string data, find customer with 10 or more transactions, input data, dynamic rename, text to columns, transpose, identify common data fields, see how all records fit the filter, data cleansing, impute values, random sample.