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OVERVIEW
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Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves the use of techniques from statistics, data analysis, machine learning, and computer science to extract insights and knowledge from data. Data science can be applied in a wide range of fields, including business, healthcare, finance, and government, among others. The goal of data science is to turn raw data into actionable insights that can inform decision-making and improve outcomes.
DATA SCIENCE SYLLABUS
Core Data Science
- • Selecting rows/observations
- • Rounding Number
- • Selecting columns/fields
- • Merging data
- • Data aggregation
- • Data munging techniques
- • What is Python?
- • Why Python?
- • Installing Python
- • Python IDEs
- • Jupyter Notebook Overview
- • Python Basic Data types
- • Lists
- • Slicing
- • IF statements
- • Loops
- • Dictionaries
- • Tuples
- • Functions
- • Array
- • Selection by position & Labels
- • Pandas
- • Numpy
- • Sci-kit Learn
- • Mat-plot library
- • Reading CSV files
- • Saving in Python data
- • Loading Python data objects
- • Writing data to CSV file
- • Selecting rows/observations
- • Rounding Number
- • Selecting columns/fields
- • Merging data
- • Data aggregation
- • Data munging techniques
- • Central Tendency
- o Mean
- o Median
- o Mode
- o Skewness
- o Normal Distribution
- • Probability Basics
- o What does it mean by probability?
- o Types of Probability
- o ODDS Ratio?
- • Standard Deviation
- o Data deviation & distribution
- o Variance
- • Bias variance Tradeoff
- o Underfitting
- o Overfitting
- • Distance metrics
- o Euclidean Distance
- o Manhattan Distance
- • Outlier analysis
- o What is an Outlier?
- o Inter Quartile Range
- o Box & whisker plot
- o Upper Whisker
- o Lower Whisker
- o Scatter plot
- o Cook’s Distance
- • Missing Value treatment
- o What is NA?
- o Central Imputation
- o KNN imputation
- o Dummification
- • Correlation
- o Pearson correlation
- o positive & Negative correlation
- • Classification
- o Confusion Matrix
- o Precision
- o Recall
- o Specificity
- o F1 Score
- • Regression
- o MSE
- o RMSE
- o MAPE
Advance Data Science
- • 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
Tableau
- • Arameters
- • Grouping Example 1
- • Grouping Example 2
- • Edit Groups
- • Set
- • Combined Sets
- • Creating a First Report
- • Data Labels
- • Create Folders
- • Sorting Data
- • Add Totals, Subtotals and Grand Totals to Report
- • 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
- • 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
- • Calculated Fields
- • Basic Approach to Calculate Rank
- • Advanced Approach to Calculate Ra
- • Calculating Running Total
- • Filters Introduction
- • Quick Filters
- • Filters on Dimensions
- • Conditional Filters
- • Top and Bottom Filters
- • Filters on Measures
- • Context Filters
- • Slicing Fliters
- • Data Source Filters
- • Extract Filters
Skills and Experience
