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Conceptual Statistics for Data Science & Analytics

Descriptive statistics, Normal Distribution, Dispersions, Correlation, Regression, Hypothesis Testing, Forecasting .

Instructed by Sumit Saha (Ph.D)

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  • It is a complete guide of Statistics & Data Analysis concepts used in Education, Data Science and corporates with 100+ problems, and examples.
  • If you are a Research Scholar, and/student or preparing for competitive exams (CFA/CMA/CA/CS/UGC/ PhD), building strong base for life in statistics, this course is must for you
  • If you are in corporate, learn the tricks and gain the knowledge to outstand
  • Land a High-Paying Job through the practical and theoretical knowledge of the course
  • Work From Home as a Freelance consultant & Data analyst!
  • Downloadable workout Notes for competitive exams and future reference purpose
  • This course content itself is the combination of 4-5 different available courses in the market
  • Do not think you I am teaching you remotely, I am always happy to clear your doubts. Just message me anytime!

Winners don't do different things, they do things differently. Complete course guide, separate guide/link of 700+ practice questions, downloadable resources, supportive animation/videos for better understanding. 

With over 15+hours of complete statistics training, quizzes, and practical steps you can follow - this is one of the most comprehensive Statisticscourses available. We'll cover Probability,  Descriptive statistics, Inferential statistics, Hypothesis Testing, Correlation Analysis, Regression Analysis, Modelling, Ch- Squared Test,  Business Forecasting, and many more. This course is a great "value for money".

By the end of this course, you will be confidently implementing techniques across the major situations in Statistics, Business, and Data Analysis for research projects etc.

  • Absolutely No experience required
  • For all students like CFA/CMA/CA/CS/MBA/BCom/ MTech/ Ph.D/ Data Science/Corporate Learning
  • Anyone looking to start a pick up highly paid freelancing skills
  • Consultants who don't know where to get started
  • People who want a career in Business Intelligence & Data Science
  • Business executives & Business Analysts
  • People who want to learn the fundamentals of statistics
  • School/college students who wants to have strong base of Statistics
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Section 1 : Statistical Description of Data

  • Lecture 1 :
  • Concept of Statistical Description of Data Preview
  • Lecture 2 :
  • Mathematics Vs. Statistics
  • Lecture 3 :
  • Why we should learn Statistics
  • Lecture 4 :
  • History of the Statistics
  • Lecture 5 :
  • Who is the father of Statistics?
  • Lecture 6 :
  • Limitations of statistics
  • Lecture 7 :
  • Application of Data

Section 2 : Data Visualisation

  • Lecture 1 :
  • Simple and Multiple line chart
  • Lecture 2 :
  • Different types of Bar Diagrams
  • Lecture 3 :
  • Video explanation: Application of Bar charts
  • Lecture 4 :
  • Pie Chart
  • Lecture 5 :
  • Tabulation and its guidelines
  • Lecture 6 :
  • The Secret Locker & Key problem
  • Lecture 7 :
  • Concept of Frequency Distribution
  • Lecture 8 :
  • Important terms related to Frequency Distribution
  • Lecture 9 :
  • Graphical representation of Frequency Distribution, and HISTOGRAM
  • Lecture 10 :
  • Calculation of MODE from Histogram
  • Lecture 11 :
  • Frequency Polygon
  • Lecture 12 :
  • Ogives & Calculation of Median
  • Lecture 13 :
  • Ogives (continued)
  • Lecture 14 :
  • Types of frequency curves

Section 3 : Measurement of Central Tendency

  • Lecture 1 :
  • Concept of central tendency & Pethagorean Mean
  • Lecture 2 :
  • concept of Arithmetic Mean (A.M)
  • Lecture 3 :
  • Properties of A.M
  • Lecture 4 :
  • Geometric Mean (G.M)
  • Lecture 5 :
  • Properties of G.M
  • Lecture 6 :
  • Harmonic Mean (H.M)
  • Lecture 7 :
  • Properties of H.M
  • Lecture 8 :
  • Median for discrete data
  • Lecture 9 :
  • Properties of Median
  • Lecture 10 :
  • Properties of Mode
  • Lecture 11 :
  • Calculating central tendencies from open class frequency distribution
  • Lecture 12 :
  • concept of Quartiles, Deciles and Percentiles
  • Lecture 13 :
  • calculation of Quartiles, Deciles & Percentiles (discrete data)
  • Lecture 14 :
  • Calculation of Mean for group Frequency data
  • Lecture 15 :
  • using basic concept: Calculation of A.M (grouped frequency data)
  • Lecture 16 :
  • How to write Quartiles, Deciles & Percentiles formulae
  • Lecture 17 :
  • Calculation of Median for group Frequency data
  • Lecture 18 :
  • for open ended - unequal C.I data calculating Quartiles, Deciles and Percentiles
  • Lecture 19 :
  • Mode for discrete data
  • Lecture 20 :
  • Calculation of Mode for group Frequency data
  • Lecture 21 :
  • Empirical formula for easy calculation of Central tendencies
  • Lecture 22 :
  • Skewness
  • Lecture 23 :
  • Relation of Mean, Median & Mode for skewed curve
  • Lecture 24 :
  • Kutosis

Section 4 : Measurement Scales

  • Lecture 1 :
  • Categorical and Continuous data_ Nominal, Categorical, Interval & Ration Scales
  • Lecture 2 :
  • Variance & Bessel's Correction
  • Lecture 3 :
  • Fence_Wiskers_Inter Quartile Range_Outliers
  • Lecture 4 :
  • Covariance
  • Lecture 5 :
  • Population vs Sample mean
  • Lecture 6 :
  • Stem & Leaf display
  • Lecture 7 :
  • Valid & Reliable Data

Section 5 : Dispersions

  • Lecture 1 :
  • Measurement of dispersions
  • Lecture 2 :
  • Range & coefficient of Range
  • Lecture 3 :
  • Quartile Deviation & coefficient of Q.D
  • Lecture 4 :
  • Calculating QD for open ended C.I when cumulative frequency is given
  • Lecture 5 :
  • concept of Mean Deviations & coefficients of M.D
  • Lecture 6 :
  • Calculating M.D & Coefficient of M.D
  • Lecture 7 :
  • Standard Deviation
  • Lecture 8 :
  • Standard Deviation explained with animation
  • Lecture 9 :
  • Composite standard deviation
  • Lecture 10 :
  • Coefficient of variance
  • Lecture 11 :
  • Are you a selector of your national cricket team? Batsman Problem
  • Lecture 12 :
  • CV- missing frequency problem
  • Lecture 13 :
  • calculating correct mean & SD from Wrong mean & wrong S.D

Section 6 : Hypothesis Testing

  • Lecture 1 :
  • Concept of Hypothesis Testing
  • Lecture 2 :
  • Burden of proof
  • Lecture 3 :
  • Standard Error
  • Lecture 4 :
  • Why do we need Hypothesis testing?
  • Lecture 5 :
  • Difference between Std Error & std Deviation
  • Lecture 6 :
  • Type1 & Type 2 errors
  • Lecture 7 :
  • Forming Hypothesis correctly
  • Lecture 8 :
  • Examples- Forming Null & Alternate Hypothesis
  • Lecture 9 :
  • Lower, Upper & 2 tailed Test
  • Lecture 10 :
  • How to prove Hypothesis
  • Lecture 11 :
  • What if researchers set 0% error?

Section 7 : Theoretical Distribution

  • Lecture 1 :
  • Concept of data distribution
  • Lecture 2 :
  • Uniform distribution
  • Lecture 3 :
  • Binomial Distribution
  • Lecture 4 :
  • Application of Binomial Distribution
  • Lecture 5 :
  • Properties of Binomial Distribution
  • Lecture 6 :
  • Poison Distribution
  • Lecture 7 :
  • Application of Poison Distribution
  • Lecture 8 :
  • Poisson Distribution: Partial Interval
  • Lecture 9 :
  • Properties of Poison Distribution
  • Lecture 10 :
  • Normal Distribution,Standard Normal Distribution & formulae
  • Lecture 11 :
  • Normal Distribution & Standard Normal Distribution
  • Lecture 12 :
  • Z-Score & solving Business problems

Section 8 : Correlation Analysis

  • Lecture 1 :
  • Concept of Correlation Analysis
  • Lecture 2 :
  • Scattered Diagram
  • Lecture 3 :
  • Pearson's Correlation coefficient
  • Lecture 4 :
  • KP solved problem
  • Lecture 5 :
  • KP solved problem-2
  • Lecture 6 :
  • Covariance & KP correlation coefficient
  • Lecture 7 :
  • Spearmans rank Correlation Coefficient
  • Lecture 8 :
  • Spearman's Correlation Solved problem
  • Lecture 9 :
  • Coefficient of Concurrent Deviation

Section 9 : Regression Analysis

  • Lecture 1 :
  • Concept of Linear, Ordinal and Multiple Regression
  • Lecture 2 :
  • Linear Regression
  • Lecture 3 :
  • Minimum Least Square Method
  • Lecture 4 :
  • Regression Equations: 'x on y' & 'y on x'
  • Lecture 5 :
  • Regression Solved problem-1
  • Lecture 6 :
  • Predicting mom's height based on daughter's height
  • Lecture 7 :
  • Regression Solved problem-2
  • Lecture 8 :
  • Spurious Correlation & Coefficient of Determination
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Sumit Saha (Ph.D),

A Philomath, author, trainer, copyright holder, Management consultant, and a seasoned professional with 12+ years of industry, teaching, training, and research experience of 5000+ CA/CS/MBA/BCom/ SPSS/ PMP/ Corporate students till date. I am Ex-Vodafone. Reliance communication, Hughes like MNCs and worked as different capacity up to Associate Vice President (AVP)- Sales. I have reach experience in practical industry experience. My interset is in Research Methodology, Strategic Marketing, Enterprise Information System, Project Management (PMP), Business Mathematics, Business Statistics, Operation Management, Entrepreneurship, Data Science, and SPSS.
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