Data Analyst with Python Course Syllabus

About The Course:

In today's data-driven world, the ability to analyze and interpret data is more important than ever. Embark on an enriching journey into the world of Data Analyst with our comprehensive course. Covering fundamental concepts, this program guides you through Python for data manipulation, machine learning, and statistical analysis. Acquire practical skills in data cleaning, visualization, and exploratory data analysis, and delve into advanced topics like regression, clustering, and time series analysis. With hands-on experience and a focus on real-world applications, this course empowers you to become a proficient Data Analyst . Join us to enhance your expertise and excel in the dynamic field of Data Science.

Course Certification:

Upon successfully completing our Data Analyst with Python course, participants receive a prestigious certification, recognizing their proficiency in this dynamic field. This comprehensive program covers fundamental and advanced concepts, guiding participants through Python for data manipulation, machine learning, and statistical analysis. Acquiring practical skills in data cleaning, visualization, and exploratory data analysis, participants delve into advanced topics such as regression, clustering, and time series analysis. With a focus on real-world applications, this course empowers individuals to become proficient Data Analyst .

Introduction to Statistical Analysis

Counting, Probability, and Probability Distributions - Sampling Distributions - Estimation and Hypothesis Testing - Scatter Diagram - Anova and ChiSquare - Imputation Techniques - Data Cleaning - Correlation and Regression.

Data Analytics Overview

Importance of Data Analytics - Types of Data Analytics - Descriptive Analytics - Diagnostic Analytics - Predictive Analytics - Prescriptive Analytics - Benefits of Data Analytics - Data Visualization for Decision Making - Data Types, Measure Of Central Tendency, Measures of Dispersion - Graphical Techniques, Skewness & Kurtosis, Box Plot - Descriptive Stats - Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence Interval.

Excel: Basics to Advanced

Excel tutorial - Text to Column Concatenate - The Concatenate Function - The Right Function with Concatenation - Absolute Cell References - Data Validation - Time and Date Calculations - Conditional Formatting - Exploring Styles and Clearing Formatting - Using Conditional Formattingc to Hide Cells - Using the IF Function - Changing the “Value if false” Condition to Text - Pivot Tables - Creating a Pivot Table - Specifying PivotTable Data - Changing a PivotTables Calculation - Filtering and Sorting a PivotTable - Creating a PivotChart - Grouping Items -Updating a PivotTable - Formatting a PivotTable - Using Slicers - Charts - Creating a Simple Chart - Charting Non-Adjacent Cells - Creating a Chart Using the Chart Wizard - Modifying Charts -Moving an Embedded Chart - Sizing an Embedded Chart -Changing the Chart Type -Chart Types -Changing the Way Data is Displayed - Moving the Legend - Formatting Charts - Adding Chart Items - Formatting All Text - Formatting and Aligning Numbers - Formatting the Plot Area - Formatting Data Markers - Pie Charts - Creating a Pie Chart - Moving the Pie Chart to its Own Sheet - Adding Data Labels - Exploding a Slice of a Pie Chart - Data Analysis Overview - Types of Data Analysis - Data Analysis Process - Working with Range Names - Copying Name using Formula Autocomplete - Range Name Syntax Rules - Creating Range Names - Creating Names for Constants - Managing Names - Scope of a Name - Editing Names - Applying Names - Using Names in a Formula - Viewing Names in a Workbook - Copying Formulas with Names - Difference between Tables and Ranges - Create Table - Table Name - Managing Names in a Table - Table Headers replacing Column Letters - Propagation of a Formula in a Table - Resize Table - Remove Duplicates - Convert to Range - Table Style Options - Table Styles - Cleaning Data with Text Functions - Removing Unwanted Characters from Text - Extracting Data Values from Text - Formatting Data with Text Functions - Date Formats - Conditional Formatting - Sorting - Filtering - Lookup Functions – Pivoting.

SQL

Introduction to Oracle Database - Retrieve Data using the SQL SELECT Statement - Learn to Restrict and Sort Data - Usage of Single-Row Functions to Customize Output - Invoke Conversion Functions and Conditional Expressions - Aggregate Data Using the Group Functions - Display Data from Multiple Tables Using Joins - Use Sub-Queries to Solve Queries - The SET Operators - Data Manipulation is Statements - Use of DDL Statements to Create and Manage Tables - Other Schema Objects - Control User Access - Management of Schema Objects - Manage Objects with Data Dictionary Views - Manipulate Large Data Sets - Data Management in Different Time Zones - Retrieve Data Using Sub-queries - Regular Expression Support.

Tableau

Module 1: Tableau Course Material

Start Page - Show Me - Connecting to Excel Files - Connecting to Text Files - Connect to Microsoft SQL Server - Connecting to Microsoft Analysis Services - Creating and Removing Hierarchies -Bins - Joining Tables - Data Blending

Module 2: Tableau Basic Reports

Parameters - Grouping Example 1 - Grouping Example 2 - Edit Groups - Set - Combined Sets - Creating a First Report -Data Labels -Create Folders - Sorting Data - Add Totals, Sub Totals and Grand Totals to Report

Module 3: Tableau Charts

Area Chart - Bar Chart - Box Plot - Bubble Chart - Bump Chart - Bullet Graph - Circle Views - Dual Combination Chart -Dual Lines Chart - Funnel Chart - Traditional Funnel Charts - Gantt Chart - Grouped Bar or Side by Side Bars Chart - Heatmap - Highlight Table - Histogram - Cumulative Histogram - Line Chart - Lollipop Chart - Pareto Chart - Pie Chart - Scatter Plot - Stacked Bar Chart - Text Label - Tree Map - Word Cloud - Waterfall Chart.

MODULE 4: LEARN TABLEAU ADVANCED REPORTS

Dual Axis Reports - Blended Axis - Individual Axis - Add Reference Lines - Reference Bands - Reference Distributions - Basic Maps - Symbol Map - Use Google Maps - Mapbox Maps as a Background Map - WMS Server Map as a Background Map.

Module 5: Learn Tableau Calculations & Filters

Calculated Fields - Basic Approach to Calculate Rank - Advanced Approach to Calculate Rank - Calculating Running Total - Filters Introduction - Quick Filters - Filters on Dimensions - Conditional Filters - Top and Bottom Filters - Filters on Measures - Context Filters - Slicing Filters - Data Source Filters - Extract Filters.

Module 6: Learn Tableau Dashboards

Create a Dashboard - Format Dashboard Layout - Create a Device Preview of a Dashboard - Create Filters on Dashboard - Dashboard Objects - Create a Story

Module 7: Server

Tableau Online - Overview of Tableau Server - Publishing Tableau objects and scheduling/subscription.

Power BI

Module 1: Introduction to Power BI

Get Started with Power BI - Overview: Power BI concepts - Sign up for Power BI - Overview: Power BI data sources - Connect to a SaaS solution - Upload a local CSV file - Connect to Excel data that can be refreshed - Connect to a sample - Create a Report with Visualizations - Explore the Power BI portal

Module 2: Viz and Tiles

Using visualizations, create a new report, create and arrange visualizations, format a visualization, create chart visualizations, use text, map, and gauge visualizations, and save a report. Utilize a slicer to filter visualizations, sort, copy, and paste visualizations, and download and use a custom visual from the gallery.

Module 3: Reports and Dashboards

Rename and delete report pages, add a filter to a page or report, set visualization interactions, print a report page, send a report to PowerPoint. Create a Dashboard, create and manage dashboards, pin a report tile to a dashboard, pin a live report page to a dashboard, pin a tile from another dashboard, pin an Excel element to a dashboard, manage pinned elements in Excel. Add a tile to a dashboard, build a dashboard with Quick Insights, set a Featured (default) dashboard. Ask Questions about Your Data, ask a question with Power BI Q&A, tweak your dataset for Q&A, enable Cortana for Power BI.

Module 4: Publishing Workbooks and Workspace

Publish a report to the web, manage published reports, share a dashboard, create an app workspace and add users, use an app workspace, publish an app, create a QR code to share a tile, embed a report in SharePoint Online.

Module 5: Other Power BI Components and Table Relationship

Get Power BI for mobile, view reports and dashboards in the iPad app, use workspaces in the mobile app, sharing from Power BI Mobile. Use Power BI Desktop, install and launch Power BI Desktop, get data, reduce data, transform data, relate tables, get Power BI Desktop data with the Power BI service, export a report from Power BI service to Desktop.

Module 6: DAX functions

Date and time functions, time intelligence functions, filter functions, information functions, logical functions, math & trig functions, parent and child functions, text functions. Python Syntax, variables, data types, and basic operations. Introduction to NumPy, NumPy arrays, and basic operations on arrays. Introduction to Pandas, Series and DataFrames, data manipulation, and analysis. Data Cleaning and Preprocessing, handling missing data, dealing with duplicates, and data transformation. Data Visualization with Matplotlib and Seaborn, creating various types of plots and charts for data exploration. Exploratory Data Analysis (EDA), statistical summaries, distribution analysis, and correlation exploration. Introduction to Jupyter Notebooks, using Jupyter for interactive data analysis and visualization. Introduction to Statistics with Python, descriptive statistics, inferential statistics, and hypothesis testing. Introduction to Machine Learning with Scikit-Learn, basic concepts, model training, and evaluation. Linear Regression, understanding and implementing simple and multiple linear regression. Logistic Regression, binary classification using logistic regression. Clustering Algorithms, K-Means clustering, and hierarchical clustering. Dimensionality Reduction, Principal Component Analysis (PCA). Time Series Analysis, handling time-series data, trends, and seasonality. Web Scraping with Python, basics of web scraping for data acquisition. Integration with Databases, connecting Python to databases (e.g., SQLite, MySQL) for data retrieval and storage.