- Instructor: skillcircle
- Lectures: 116
- Duration: 80 hours
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
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Introduction to Data Science with Python ( Induction)
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Lecture 2.1What is Analytics & Data Science?
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Lecture 2.2Common Terms in Analytics
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Lecture 2.3What is data
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Lecture 2.4Classification of data
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Lecture 2.5Relevance in industry and need of the hour
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Lecture 2.6Types of problems and business objectives in various industries
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Lecture 2.7How leading companies are harnessing the power of analytics?
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Lecture 2.8Critical success drivers
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Lecture 2.9Overview of analytics tools & their popularity
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Lecture 2.10Analytics Methodology & problem solving framework
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Lecture 2.11List of steps in Analytics projects
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Lecture 2.12Identify the most appropriate solution design for the given problem statement
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Lecture 2.13Project plan for Analytics project & key milestones based on effort estimates
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Lecture 2.14Build Resource plan for analytics project
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Lecture 2.15Why Python for data science?
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Python Core
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Lecture 3.1Introduction to python and comparison with other programming language
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Lecture 3.2Installation of Anaconda Distribution and other python IDE
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Lecture 3.3Python Objects ,Numbers & Booleans, Strings, Container objects, Mutability of objects
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Lecture 3.4Concept of Packages/Libraries – Important packages(Pandas, NumPy, SciPy, Scikit-learn, Seaborn, Matplotlib)
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Lecture 3.5Operators – Arithmetic, Bitwise, Comparison and Assignment Operators
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Lecture 3.6Conditions (If else, if-elif)
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Lecture 3.7Loops( While,For)
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Lecture 3.8Break and Continue Statement
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Lecture 3.9Range Functions
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String Objects and Collection
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Lecture 4.1String Object Basics
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Lecture 4.2String Methods
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Lecture 4.3Splitting and joining strings
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Lecture 4.4String Format Functions
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Lecture 4.5List object Basics
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Lecture 4.6List Methods
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Tuples, Set, Dictionaries & Functions
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Lecture 5.1Tuples, Sets, Dictionary object basics, Dictionary, Object methods, Dictionary
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Lecture 5.2view objects
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Lecture 5.3Functions basics, Iterators
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Lecture 5.4Generator functions
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Lecture 5.5Lambda Functions
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Lecture 5.6Map, Reduce & filter functions
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OOPS concepts & Working with Files
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Lecture 6.1OOPS basic concepts
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Lecture 6.2Creating classes and objects
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Lecture 6.3Working with files
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Lecture 6.4Reading and Writing files
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Database
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Lecture 7.1MongoDB
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Lecture 7.2Python SQL
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Python Pandas
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Lecture 8.1Python pandas – series
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Lecture 8.2Python pandas – Dataframe
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Lecture 8.3Python pandas – Basic Functionality
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Lecture 8.4Python pandas- Reindexing
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Lecture 8.5Python pandas – Iteration
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Lecture 8.6Python pandas – Sorting
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Lecture 8.7Python pandas – Working with Text data
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Lecture 8.8Python pandas – options and customization
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Lecture 8.9Python pandas – Indexing and selecting data
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Lecture 8.10Python pandas – Date Functionality
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Lecture 8.11Python pandas – Categorical Data
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Lecture 8.12Python pandas – Visulaization
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Data Manipulation : Cleansing - Munging
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Lecture 9.1Cleansing Data with Python
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Lecture 9.2Filling missing values using lambda function and concept of Skewness
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Lecture 9.3Data Manipulation steps (Sorting, filtering, duplicates, merging, appending subsetting, derived variables, sampling, Data type conversions, renaming, v formatting etc)
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Data Analysis : Visualization Using Python
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Lecture 10.1Introduction exploratory data analysis
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Lecture 10.2Descriptive statistics, Frequency Tables and summarization
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Lecture 10.3Univariate Analysis (Distribution of data & Graphical Analysis)
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Lecture 10.4Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
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Lecture 10.5Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
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Lecture 10.6Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
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Introduction To Statistics
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Lecture 11.1Descriptive Statistics
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Lecture 11.2Sample vs Population Statistics
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Lecture 11.3Random variables
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Lecture 11.4Probability distribution functions
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Lecture 11.5Expected value
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Lecture 11.6Normal distribution
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Lecture 11.7Gaussian distribution
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Lecture 11.8Z-score
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Lecture 11.9Central limit theorem
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Lecture 11.10Spread and Dispersion
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Lecture 11.11Hypothesis testing
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Lecture 11.12Z-stats vs T-stats
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Lecture 11.13Type 1 & Type 2 error
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Lecture 11.14Confidence Interval
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Lecture 11.15ANOVA Test
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Lecture 11.16Chi Square Test
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Lecture 11.17T-test 1-Tail 2-Tail Test
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Lecture 11.18Correlation and Covariance
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Introduction To Predictive Modeling
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Lecture 12.1Concept of model in analytics and how it is used?
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Lecture 12.2Common terminology used in Analytics & Modeling process
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Lecture 12.3Popular Modeling algorithms
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Lecture 12.4Types of Business problems – Mapping of Techniques
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Lecture 12.5Different Phases of Predictive Modeling
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Data Exploration For Modeling
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Lecture 13.1Need for structured exploratory data
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Lecture 13.2EDA framework for exploring the data and identifying any problems with the v data by the help of pairplot.
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Lecture 13.3Identify missing data
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Lecture 13.4Identify outliers data
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Lecture 13.5Visualize the data trends and patterns
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Data Preparation
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Lecture 14.1Merging
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Lecture 14.2Normalizing the data
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Lecture 14.3Feature Engineering
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Lecture 14.4Feature Selection
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Lecture 14.5Feature scaling using Standard Scaler.
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Lecture 14.6Label encoding
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Lecture 14.7Need of Data preparation
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Lecture 14.8Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values
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Lecture 14.9Dummy creation – Variable Reduction
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Lecture 14.10Variable Reduction Techniques – Factor & PCA Analysis
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Tableau
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Lecture 15.1Working with tableau
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Lecture 15.2Data organisation
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Lecture 15.3Creation of parameters
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Lecture 15.4Advanced visualization
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Lecture 15.5Dashboard data presentation
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Projects
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Lecture 16.1Managing credit card Risks
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Lecture 16.2Bank Loan default classification
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Lecture 16.3Youtube Viewers predictions
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Lecture 16.4Super store Analytics (E-commerce)
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Lecture 16.5Buying and selling cars prediction(like OLX process)
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Lecture 16.6Advanced House price prediction
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Lecture 16.7Analytics on HR decisions
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Lecture 16.8Twitter Analysis
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Lecture 16.9AI Chat Box
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Lecture 16.10Flight price prediction
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