- Instructor: skillcircle
- Lectures: 95
- Duration: 120 hours
Categories: live
Data field is evolving each day and new opportunities are coming everyday. Keeping this in mind we have designed a Master Program in Data science under the exceptional mentorship of leading data scientists from the data fraternity.
For 6 months you will be learning, practicing data science with Capstone projects and hands on practical exposure.
After the end of cohort, you will be working as data scientist as Full time candidature or as a Trainee with some leading data science company.
Eligibility
- Applicants should have 50% or above in Xth, XIIth and Bachelor’s degree.
- The program is open for candidates in their final semester and recent graduates with 0-3 years of experience.
- The Data Science Course is ideal for candidates with a graduation in a quantitative discipline like engineering, mathematics, commerce, sciences, statistics, economics, BBA,BCOM,MBA,BMS,BBS
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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 v objects
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Lecture 3.4Concept of Packages/Libraries – Important packages(Pandas, NumPy, SciPy, v 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 v view objects, Functions basics, Iterators, Generator functions, Lambda Functions, Map, 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 – Visualization
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Data Manipulation
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Lecture 9.1Cleansing – Munging Cleansing 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, 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 Co-variance
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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 data by the help of pair plot.
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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- Dummy creation – Variable Reduction
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Lecture 14.9Variable Reduction Techniques – Factor & PCA Analysis
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