SkillDegree in Data Analytics

Instructor: skillcircle
Lectures: 116
Duration: 80 hours

Categories: live

With data being at the core of the technology, data analytics has gained humongous popularity over the past few years and has become a hot topic in the market. With billions of data getting generated every day, demand for data analysts with the right skill set and knowledge will keep increasing. According to Harvard Business Review, data science is the sexiest job of the 21st century.

We have crafted a Skill Degree in Data analytics specially for professionals who want to leverage this multi billion dollar industry with dedicated mentorship from the best data folks from our country.

Companies Hiring our Candidates :

CRED, HSBC, KOTAK BANK, Razor Pay, Policy Bazaar, Oyo Rooms, TATA IQ, CRED, KPMG, Genpact, IBM, Info Gain, UBER, Renault , Accenture, Bewakoof.com, Swiggy, TVS, Mahindra, Paisa Bazaar

Program Mentors

Mr. Shivam Ahuja
Mr. Mahadev Shekhawat , Data Analyst Airtel Africa
Disha , Data scientist AirAsia
Himanshu, Data Scientist Swiggy
Nikhil Kumar Singh Data Scientist at MobiKwik
Rishabh Gupta Senior Data Scientist at HDFC Life

Introduction to Data Science with Python ( Induction) 0/15
- Lecture 2.1
  
  What is Analytics & Data Science?
- Lecture 2.2
  
  Common Terms in Analytics
- Lecture 2.3
  
  What is data
- Lecture 2.4
  
  Classification of data
- Lecture 2.5
  
  Relevance in industry and need of the hour
- Lecture 2.6
  
  Types of problems and business objectives in various industries
- Lecture 2.7
  
  How leading companies are harnessing the power of analytics?
- Lecture 2.8
  
  Critical success drivers
- Lecture 2.9
  
  Overview of analytics tools & their popularity
- Lecture 2.10
  
  Analytics Methodology & problem solving framework
- Lecture 2.11
  
  List of steps in Analytics projects
- Lecture 2.12
  
  Identify the most appropriate solution design for the given problem statement
- Lecture 2.13
  
  Project plan for Analytics project & key milestones based on effort estimates
- Lecture 2.14
  
  Build Resource plan for analytics project
- Lecture 2.15
  
  Why Python for data science?
Python Core 0/9
- Lecture 3.1
  
  Introduction to python and comparison with other programming language
- Lecture 3.2
  
  Installation of Anaconda Distribution and other python IDE
- Lecture 3.3
  
  Python Objects ,Numbers & Booleans, Strings, Container objects, Mutability of objects
- Lecture 3.4
  
  Concept of Packages/Libraries – Important packages(Pandas, NumPy, SciPy, Scikit-learn, Seaborn, Matplotlib)
- Lecture 3.5
  
  Operators – Arithmetic, Bitwise, Comparison and Assignment Operators
- Lecture 3.6
  
  Conditions (If else, if-elif)
- Lecture 3.7
  
  Loops( While,For)
- Lecture 3.8
  
  Break and Continue Statement
- Lecture 3.9
  
  Range Functions
String Objects and Collection 0/6
- Lecture 4.1
  
  String Object Basics
- Lecture 4.2
  
  String Methods
- Lecture 4.3
  
  Splitting and joining strings
- Lecture 4.4
  
  String Format Functions
- Lecture 4.5
  
  List object Basics
- Lecture 4.6
  
  List Methods
Tuples, Set, Dictionaries & Functions 0/6
- Lecture 5.1
  
  Tuples, Sets, Dictionary object basics, Dictionary, Object methods, Dictionary
- Lecture 5.2
  
  view objects
- Lecture 5.3
  
  Functions basics, Iterators
- Lecture 5.4
  
  Generator functions
- Lecture 5.5
  
  Lambda Functions
- Lecture 5.6
  
  Map, Reduce & filter functions
OOPS concepts & Working with Files 0/4
- Lecture 6.1
  
  OOPS basic concepts
- Lecture 6.2
  
  Creating classes and objects
- Lecture 6.3
  
  Working with files
- Lecture 6.4
  
  Reading and Writing files
Database 0/2
- Lecture 7.1
  
  MongoDB
- Lecture 7.2
  
  Python SQL
Python Pandas 0/12
- Lecture 8.1
  
  Python pandas – series
- Lecture 8.2
  
  Python pandas – Dataframe
- Lecture 8.3
  
  Python pandas – Basic Functionality
- Lecture 8.4
  
  Python pandas- Reindexing
- Lecture 8.5
  
  Python pandas – Iteration
- Lecture 8.6
  
  Python pandas – Sorting
- Lecture 8.7
  
  Python pandas – Working with Text data
- Lecture 8.8
  
  Python pandas – options and customization
- Lecture 8.9
  
  Python pandas – Indexing and selecting data
- Lecture 8.10
  
  Python pandas – Date Functionality
- Lecture 8.11
  
  Python pandas – Categorical Data
- Lecture 8.12
  
  Python pandas – Visulaization
Data Manipulation : Cleansing - Munging 0/3
- Lecture 9.1
  
  Cleansing Data with Python
- Lecture 9.2
  
  Filling missing values using lambda function and concept of Skewness
- Lecture 9.3
  
  Data Manipulation steps (Sorting, filtering, duplicates, merging, appending subsetting, derived variables, sampling, Data type conversions, renaming, v formatting etc)
Data Analysis : Visualization Using Python 0/6
- Lecture 10.1
  
  Introduction exploratory data analysis
- Lecture 10.2
  
  Descriptive statistics, Frequency Tables and summarization
- Lecture 10.3
  
  Univariate Analysis (Distribution of data & Graphical Analysis)
- Lecture 10.4
  
  Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Lecture 10.5
  
  Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
- Lecture 10.6
  
  Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
Introduction To Statistics 0/18
- Lecture 11.1
  
  Descriptive Statistics
- Lecture 11.2
  
  Sample vs Population Statistics
- Lecture 11.3
  
  Random variables
- Lecture 11.4
  
  Probability distribution functions
- Lecture 11.5
  
  Expected value
- Lecture 11.6
  
  Normal distribution
- Lecture 11.7
  
  Gaussian distribution
- Lecture 11.8
  
  Z-score
- Lecture 11.9
  
  Central limit theorem
- Lecture 11.10
  
  Spread and Dispersion
- Lecture 11.11
  
  Hypothesis testing
- Lecture 11.12
  
  Z-stats vs T-stats
- Lecture 11.13
  
  Type 1 & Type 2 error
- Lecture 11.14
  
  Confidence Interval
- Lecture 11.15
  
  ANOVA Test
- Lecture 11.16
  
  Chi Square Test
- Lecture 11.17
  
  T-test 1-Tail 2-Tail Test
- Lecture 11.18
  
  Correlation and Covariance
Introduction To Predictive Modeling 0/5
- Lecture 12.1
  
  Concept of model in analytics and how it is used?
- Lecture 12.2
  
  Common terminology used in Analytics & Modeling process
- Lecture 12.3
  
  Popular Modeling algorithms
- Lecture 12.4
  
  Types of Business problems – Mapping of Techniques
- Lecture 12.5
  
  Different Phases of Predictive Modeling
Data Exploration For Modeling 0/5
- Lecture 13.1
  
  Need for structured exploratory data
- Lecture 13.2
  
  EDA framework for exploring the data and identifying any problems with the v data by the help of pairplot.
- Lecture 13.3
  
  Identify missing data
- Lecture 13.4
  
  Identify outliers data
- Lecture 13.5
  
  Visualize the data trends and patterns
Data Preparation 0/10
- Lecture 14.1
  
  Merging
- Lecture 14.2
  
  Normalizing the data
- Lecture 14.3
  
  Feature Engineering
- Lecture 14.4
  
  Feature Selection
- Lecture 14.5
  
  Feature scaling using Standard Scaler.
- Lecture 14.6
  
  Label encoding
- Lecture 14.7
  
  Need of Data preparation
- Lecture 14.8
  
  Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values
- Lecture 14.9
  
  Dummy creation – Variable Reduction
- Lecture 14.10
  
  Variable Reduction Techniques – Factor & PCA Analysis
Tableau 0/5
- Lecture 15.1
  
  Working with tableau
- Lecture 15.2
  
  Data organisation
- Lecture 15.3
  
  Creation of parameters
- Lecture 15.4
  
  Advanced visualization
- Lecture 15.5
  
  Dashboard data presentation
Projects 0/10
- Lecture 16.1
  
  Managing credit card Risks
- Lecture 16.2
  
  Bank Loan default classification
- Lecture 16.3
  
  Youtube Viewers predictions
- Lecture 16.4
  
  Super store Analytics (E-commerce)
- Lecture 16.5
  
  Buying and selling cars prediction(like OLX process)
- Lecture 16.6
  
  Advanced House price prediction
- Lecture 16.7
  
  Analytics on HR decisions
- Lecture 16.8
  
  Twitter Analysis
- Lecture 16.9
  
  AI Chat Box
- Lecture 16.10
  
  Flight price prediction

skillcircle

Shivam is an entrepreneur and marketing consultant for different startups.

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SkillDegree in Data Analytics

Companies Hiring our Candidates :

Program Mentors

Introduction to Data Science with Python ( Induction) 0/15

Python Core 0/9

String Objects and Collection 0/6

Tuples, Set, Dictionaries & Functions 0/6

OOPS concepts & Working with Files 0/4

Database 0/2

Python Pandas 0/12

Data Manipulation : Cleansing - Munging 0/3

Data Analysis : Visualization Using Python 0/6

Introduction To Statistics 0/18

Introduction To Predictive Modeling 0/5

Data Exploration For Modeling 0/5

Data Preparation 0/10

Tableau 0/5

Projects 0/10

Price

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