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AD3491 Fundamentals of data science analysis
Unit 11. Benefits and uses and process. of data science 2. cleansing, integrating, and transforming data3.Data analysis, building applicationsDon't share as screenshot Stuff SEctor
UNIT-21. Correlation,scatter plots,regression,least squares regression lineDon't share as screenshot Stuff SEctor2.Normal Distributions and Standard (z) ScoresUNIT-31. random sampling, Sampling distribution,standard error of the mean2. z-test procedure,decision rule.Don't share as screenshot Stuff SEctorUNIT-41. two-factor ANOVA,Introduction to chi-square tests,experiments**Don't share as screenshot Stuff SEctor2. sampling distribution of t – t-test procedure,three F test**UNIT-51. weighted resampling. Regression using StatsModels
2.serial correlation, autocorrelation,TOTA
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**Very important questions are bolded and may be asked based on this topic
PART-C
1.Compulsory Questions {a case study where the student will have to read and analyse the subject }mostly asked from unit 2, 5(OR) a situation given and you have to answer on your own
AD3491 Fundamentals of data science analysis
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**Very important questions are bolded and may be asked based on this topic
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*These questions are expected for the exams This may or may not be asked for exams All the best.... from admin Santhosh
Thanks for your love and support guys keep supporting and share let the Engineers know about Us and leave a comment below for better improvements If there is any doubt feel free to ask me I will clear if I can or-else I will say some solutions ..get me through WhatsApp for instant updates ~$tuff$£ctorSYllabuSUNIT I INTRODUCTION TO DATA SCIENCE
Need for data science – benefits and uses – facets of data – data science process – setting the
research goal – retrieving data – cleansing, integrating, and transforming data – exploratory data
analysis – build the models – presenting and building applications.
UNIT II DESCRIPTIVE ANALYTICS
Frequency distributions – Outliers –interpreting distributions – graphs – averages - describing
variability – interquartile range – variability for qualitative and ranked data - Normal distributions – z
scores –correlation – scatter plots – regression – regression line – least squares regression line –
standard error of estimate – interpretation of r2 – multiple regression equations – regression toward
the mean.
UNIT III INFERENTIAL STATISTICS
Populations – samples – random sampling – Sampling distribution- standard error of the mean -
Hypothesis testing – z-test – z-test procedure –decision rule – calculations – decisions –
interpretations - one-tailed and two-tailed tests – Estimation – point estimate – confidence interval –
level of confidence – effect of sample size.
UNIT IV ANALYSIS OF VARIANCE
t-test for one sample – sampling distribution of t – t-test procedure – t-test for two independent
samples – p-value – statistical significance – t-test for two related samples. F-test – ANOVA – Two-
factor experiments – three f-tests – two-factor ANOVA –Introduction to chi-square tests.
UNIT V PREDICTIVE ANALYTICS
Linear least squares – implementation – goodness of fit – testing a linear model – weighted
resampling. Regression using StatsModels – multiple regression – nonlinear relationships – logistic
regression – estimating parameters – Time series analysis – moving averages – missing values –
serial correlation – autocorrelation. Introduction to survival analysis.
TOTA
UNIT I INTRODUCTION TO DATA SCIENCE
Need for data science – benefits and uses – facets of data – data science process – setting the
research goal – retrieving data – cleansing, integrating, and transforming data – exploratory data
analysis – build the models – presenting and building applications.
UNIT II DESCRIPTIVE ANALYTICS
Frequency distributions – Outliers –interpreting distributions – graphs – averages - describing
variability – interquartile range – variability for qualitative and ranked data - Normal distributions – z
scores –correlation – scatter plots – regression – regression line – least squares regression line –
standard error of estimate – interpretation of r2 – multiple regression equations – regression toward
the mean.
UNIT III INFERENTIAL STATISTICS
Populations – samples – random sampling – Sampling distribution- standard error of the mean -
Hypothesis testing – z-test – z-test procedure –decision rule – calculations – decisions –
interpretations - one-tailed and two-tailed tests – Estimation – point estimate – confidence interval –
level of confidence – effect of sample size.
UNIT IV ANALYSIS OF VARIANCE
t-test for one sample – sampling distribution of t – t-test procedure – t-test for two independent
samples – p-value – statistical significance – t-test for two related samples. F-test – ANOVA – Two-
factor experiments – three f-tests – two-factor ANOVA –Introduction to chi-square tests.
UNIT V PREDICTIVE ANALYTICS
Linear least squares – implementation – goodness of fit – testing a linear model – weighted
resampling. Regression using StatsModels – multiple regression – nonlinear relationships – logistic
regression – estimating parameters – Time series analysis – moving averages – missing values –
serial correlation – autocorrelation. Introduction to survival analysis.
TOTA