Curriculum
- 25 Sections
- 241 Lessons
- 15 Weeks
Expand all sectionsCollapse all sections
- Introduction to Data Science & Analytics TechniquesIn this Chapter you will learn Introduction of all the Modules which we are covering in this course.13
- 1.1Introduction to Data Science50 Minutes
- 1.2Introduction to Python
- 1.3Data Analysis Pipeline
- 1.4What is Data Extraction
- 1.5Types of Data
- 1.6Raw and Processed Data
- 1.7Data Wrangling
- 1.8Overview of the Analytics Techniques
- 1.9Analytics
- 1.10Business Analytics
- 1.11Business Intelligence
- 1.12Industry Examples
- 1.13Assessment :: Introduction to Data Science & Analytics Techniques50 Minutes15 Questions
- Introduction to Data VisualizationIn this section you will learn an overview of Tools and techniques required to Implement your project.5
- Fundamentals of ExcelDilucide gravius hanc tanta vituperanda censore stoicorum piscis frugaliter tardeve sustinebit epulis5
- 3.0Introduction :: GUI :: Cell Referencing :: Freeze panes :: Sum function
- 3.1Useful functions :: counting functions :: Summing functions :: Averaging functions :: Rounding functions
- 3.2Sorting and filtering :: Multi-level sort :: Custom filter
- 3.3Duplicates :: Remove duplicates
- 3.9Assessment :: Fundamentals of Excel40 Minutes15 Questions
- Data Cleaning & Working with Conditions using ExcelSe institutum elegantiam debeant possint apud quibusnam sapientia fama ornatu me diligimus4
- 4.0Working with text :: Concatenate :: Left, right, upper, lower
- 4.1Working with conditions :: Conditional Formatting :: Logical Operations :: IF, AND, OR, Nested IF
- 4.2Data functions :: Splitting :: Creating dates
- 4.9Assessment :: Data Cleaning & Working with Conditions using Excel10 Minutes12 Questions
- Data Manipulation using Advanced ExcelVesperum vestri reddes splendidior pericli ipsius ceteros gubernando tui deseruit constituta soletis3
- Data Analysis & Visualization using Advanced ExcelEtiamsi donan philosophorum scribebamus sensitque at varietate quandam praestantia sequens5
- 6.0What-if-Analysis :: Goal Seek :: Data Table :: Scenario Manager
- 6.1Solver Plug-in
- 6.2Pivot tables :: Dimensions & Measures :: Multi-layer Pivot Table :: Summarize Values by :: Show Values as Grouping
- 6.3Charts :: Various Charts :: Pivot Charts :: Combo Charts :: Sparklines
- 6.10Assessment :: Data Analysis & Visualization using Advanced Excel10 Minutes14 Questions
- Introduction to Statistics & Application in ExcelMetrodori uterque totum optimum tribunus pauca volo contenti utilitatem chrysippo tanta habetur roges8
- Introduction to MS SQL & MS AccessEmolumento istic voluptas perfecit sequatur obscurari disceptari oratione fac attendere negabat dilucide utebare vestigium est5
- SQL Queries & ViewsSordidos illum luci patefactio solere vocas expectoque latina docilitas terra eum intuens dolere ingenii socium3
- Python ProgrammingFrom this section you will start learning of Python from basics to Advance. So, Please mentally prepare your self with all the basic concepts which you have learnt from the previous sections.28
- 10.0Installation
- 10.1Python – Syntax
- 10.2Python – Variables and Datatypes
- 10.3Python – Numbers
- 10.4Strings
- 10.5Sequences
- 10.6List
- 10.7Tuples
- 10.8Ranges
- 10.9Dictionary
- 10.10Sets
- 10.11Operators
- 10.12If..Else.. Statements
- 10.13For Loop
- 10.14While Loop
- 10.15Break
- 10.16Continue
- 10.17Pass
- 10.18Date & Time
- 10.19Functions
- 10.20Packages and modules
- 10.21Reading a File
- 10.22Writing into File
- 10.23Class & Objects
- 10.24Python – Exceptions
- 10.25Regular Exp
- 10.26Mathematics
- 10.27Assessment :: Python Fundamentals10 Minutes0 Questions
- Python MySQLIn this section you will learn about different MySQL queries using Python. It will help you directly in industry.10
- NumpyIn this section you will learn about NumPy Module of Python.16
- 12.0ndarray
- 12.1Array Creation
- 12.2Data Type Objects
- 12.3Data type Object (dtype) in NumPy
- 12.4Indexing
- 12.5Basic Slicing and Advanced Indexing
- 12.6Iterating Over Array
- 12.7Binary Operations
- 12.8Mathematical Function
- 12.9String Operations
- 12.10Linear Algebra
- 12.11Sorting, Searching and Counting
- 12.12Set 1 (Introduction)
- 12.13Set 2 (Advanced)
- 12.14Multiplication of two Matrices in Single line using Numpy in Python
- 12.15Assessment :: NumPy
- Pandas DataFrameIn this section you will learn complete Pandas DataFrame module. This section is most important section for Data Analytics and Data Science Career.11
- 13.0Creating a Pandas DataFrame
- 13.1Dealing with Rows and Columns in Pandas DataFrame
- 13.2Indexing and Selecting Data with Pandas
- 13.3Boolean Indexing in Pandas
- 13.4Conversion Functions in Pandas DataFrame
- 13.5Iterating over rows and columns in Pandas DataFrame
- 13.6Working with Missing Data in Pandas
- 13.7Working With Text Data
- 13.8Working with Dates and Times
- 13.9Merging, Joining and Concatenating
- 13.10Assessment :: Pandas DataFrame10 Minutes0 Questions
- Data Analysis6
- Object-Oriented ConceptsIn this section you will learn OOPs concepts using python. This section is again a very important for industry career.19
- 15.0Class, Object and Members
- 15.1Data Hiding and Object Printing
- 15.2Inheritance, examples of an object, subclass and super
- 15.3Polymorphism in Python
- 15.4Class and static variable in Python
- 15.5Class method and static method in Python
- 15.6Changing class members
- 15.7Constructors in Python
- 15.8Destructors in Python
- 15.9First-class function
- 15.10str() vs repr()
- 15.11str() vs vpr()
- 15.12Metaprogramming with metaclasses
- 15.13Class and instance attribute
- 15.14Reflection
- 15.15Barrier objects
- 15.16Timer objects
- 15.17Garbage collection
- 15.18Assessment :: Object-Oriented Concepts10 Minutes0 Questions
- FunctionsThis Section will discuss about how create a function in python. This section is important because whenever you will apply ML/DL algorithms you will need to create all the test and training cases in the form of functions.16
- 16.0Functions in Python
- 16.1class method vs static method in Python
- 16.2Write an empty function in Python – pass statement
- 16.3Yield instead of Return
- 16.4Return Multiple Values
- 16.5Partial Functions in Python
- 16.6First Class functions in Python
- 16.7Precision Handling
- 16.8*args and **kwargs
- 16.9Python closures
- 16.10Function Decorators
- 16.11Decorators in Python
- 16.12Decorators with parameters in Python
- 16.13Memoization using decorators in Python
- 16.14Python bit functions on int (bit_length, to_bytes and from_bytes)
- 16.15Assessment :: Functions10 Minutes0 Questions
- Machine Learning with Python4
- Machine learning :: Statistics FundamentalsFrom this section we are starting our Machine learning Fundamentals. Please keep learning to improve your skills.25
- 18.0Graphically Displaying Single Variable
- 18.1Measures of Location
- 18.2Measures of Spread
- 18.3Displaying relationship – Bivariate Data
- 18.4Scatterplot
- 18.5Measures of association of two or more variables
- 18.6Covariance and Correlation
- 18.7Probability
- 18.8Joint Probability and independent events
- 18.9Conditional probability
- 18.10Bayes’ Theorem
- 18.11Prior, Likelihood and Posterior
- 18.12Discrete Random Variable
- 18.13Probability Distribution of Discrete Random Variable
- 18.14Binomial Distribution
- 18.15Continuous Random Variables
- 18.16Probability Distribution Function
- 18.17Uniform Distribution
- 18.18Normal Distribution
- 18.19Point Estimation
- 18.20Interval Estimation
- 18.21Hypothesis Testing
- 18.22Testing a one-sided Hypothesis
- 18.23Testing a two-sided Hypothesis
- 18.24Assessment :: Statistics Fundamentals10 Minutes0 Questions
- Regression - Intro and Data9
- 19.0Regression – Features and Labels
- 19.1Regression – Training and Testing
- 19.2Regression – Forecasting and Predicting
- 19.3Regression – Theory and how it works
- 19.4Regression – How to program the Best Fit Slope
- 19.5Regression – How to program the Best Fit Line
- 19.6Regression – R Squared and Coefficient of Determination Theory
- 19.7Model evaluation methods
- 19.8Assessment :: Regression – Intro and Data10 Minutes0 Questions
- Classification10
- Support Vector Machine Introduction9
- 21.0Vector Basics
- 21.1Support Vector Machine Fundamentals
- 21.2Constraint Optimization with Support Vector Machine
- 21.3Beginning SVM from Scratch in Python
- 21.4Support Vector Machine Optimization in Python
- 21.5Visualization and Predicting with our Custom SVM
- 21.6Kernels Introduction
- 21.7Soft Margin Support Vector Machine
- 21.8Assessment :: Support Vector Machine Introduction10 Minutes0 Questions
- Machine Learning - Clustering Introduction10
- 22.0Handling Non-Numerical Data for Machine Learning
- 22.1K-Means with Titanic Dataset
- 22.2K-Means from Scratch in Python
- 22.3Finishing K-Means from Scratch in Python
- 22.4Hierarchical Clustering with Mean Shift Introduction
- 22.5Introduction Naive Bayes Classifier
- 22.6Naive Bayes Classifier with Scikit
- 22.7Introduction into Text Classification using Naive Bayes
- 22.8Python Implementation of Text Classification
- 22.9Assessment :: Machine Learning – Clustering10 Minutes0 Questions
- Recommender Systems3
- Introduction to NLP28
- 24.0Text Preprocessing
- 24.1Noise Removal
- 24.2Lexicon Normalization
- 24.3Lemmatization
- 24.4Stemming
- 24.5Object Standardization
- 24.6Text to Features (Feature Engineering on text data)
- 24.7Syntactical Parsing
- 24.8Dependency Grammar
- 24.9Part of Speech Tagging
- 24.10Entity Parsing
- 24.11Phrase Detection
- 24.12Named Entity Recognition
- 24.13Topic Modelling
- 24.14N-Grams
- 24.15Statistical features
- 24.16TF – IDF
- 24.17Frequency / Density Features
- 24.18Readability Features
- 24.19Word Embeddings
- 24.20Important tasks of NLP
- 24.21Text Classification
- 24.22Text Matching
- 24.23Levenshtein Distance
- 24.24Phonetic Matching
- 24.25Flexible String Matching
- 24.26Important NLP libraries
- 24.27Assessment :: Introduction to NLP10 Minutes0 Questions
- Project8
Introduction :: GUI :: Cell Referencing :: Freeze panes :: Sum function
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